research paper in wireless communication

EURASIP Journal on Wireless Communications and Networking

Featured Collection from the 2022 EuCNC & 6G Summit: "6G Connectivity for a Sustainable World"

Open special issues

EURASIP Journal on Wireless Communications and Networking welcomes proposals for Special Issues on timely topics relevant to the field of signal processing. If you are interested in publishing a collection with us, please  read our guidelines here.

View our collection of published special issues here

• Most accessed

Optimal station placement based on grey wolf optimizer for regional target localization

Authors: Zewen Wang, Dexiu Hu, Jie Huang, Min Xie and Chuang Zhao

Dual-polarized IRS-assisted wireless network: relative phase modulation

Authors: Muteen Munawar and Kyungchun Lee

An adversarial environment reinforcement learning-driven intrusion detection algorithm for Internet of Things

Authors: Chahira Mahjoub, Monia Hamdi, Reem Ibrahim Alkanhel, Safa Mohamed and Ridha Ejbali

Energy efficiency maximization for active RIS-aided integrated sensing and communication

Authors: Mohamed Rihan, Alessio Zappone, Stefano Buzzi, Dirk Wübben and Armin Dekorsy

Joint beamforming and power splitting design for MISO downlink communication with SWIPT: a comparison between cell-free massive MIMO and small-cell deployments

Authors: Jain-Shing Liu, Chun-Hung Richard Lin and Wan-Ling Chang

Most recent articles RSS

View all articles

Handover management in high-dense femtocellular networks

Authors: Mostafa Zaman Chowdhury and Yeong Min Jang

A review of communication-oriented optical wireless systems

Authors: Deva K Borah, Anthony C Boucouvalas, Christopher C Davis, Steve Hranilovic and Konstantinos Yiannopoulos

Text feature extraction based on deep learning: a review

Authors: Hong Liang, Xiao Sun, Yunlei Sun and Yuan Gao

The Correction to this article has been published in EURASIP Journal on Wireless Communications and Networking 2018 2018 :42

LTE and IEEE 802.11p for vehicular networking: a performance evaluation

Authors: Zeeshan Hameed Mir and Fethi Filali

A simple block diagonal precoding for multi-user MIMO broadcast channels

Authors: Md Hashem Ali Khan, K M Cho, Moon Ho Lee and Jin-Gyun Chung

Most accessed articles RSS

Call for Special Issues

EURASIP Journal on Wireless Communications and Networking (JWCN) welcomes Special Issues on timely topics related to the field of signal processing. The objective of Special Issues is to bring together recent and high quality works in a research domain, to promote key advances in the science and applications of wireless communications and networking technologies with emphasis on original results relating to the theory and/or applications of wireless communications and networking, to provide overviews of the state-of-the-art in emerging domains.

Special issue proposals in the format of a single PDF document,  are required to be submitted by e-mail to [email protected] . Please include in the subject line ‘JWCN Special Issue Proposal’.

Read more here

EURASIP Best paper awards 2022

We are pleased to announce that the following Research Article published in EURASIP Journal on Wireless Communications and Networking has been awarded the 2022 EURASIP best paper award!

Rate-splitting multiple access for downlink communication systems: bridging, generalizing, and outperforming SDMA and NOMA Authors : Yijie Mao, Bruno Clerckx, and Victor O.K. Li

The award ceremony will be presented at the upcoming edition of EUSIPCO to be held in September 2023.

Society affiliation

The European Association for Signal Processing (EURASIP) was founded on 1 September 1978 to improve communication between groups and individuals that work within the multidisciplinary, fast growing field of signal processing in Europe and elsewhere, and to exchange and disseminate information in this field all over the world. The association exists to further the efforts of researchers by providing a learned and professional platform for dissemination and discussion of all aspects of signal processing including continuous- and discrete-time signal theory, applications of signal processing, systems and technology, speech communication, and image processing and communication. EURASIP members are entitled to a 10% discount on the article-processing charge. To claim this discount, the corresponding author must enter the membership code when prompted. This can be requested from their EURASIP representative.

Your browser needs to have JavaScript enabled to view this video

Latest Tweets

Your browser needs to have JavaScript enabled to view this timeline

Editor's Quote

Eduard Jorswieck, PhD Technische Universität Braunschweig

• Aims and Scope
• Editorial Board
• Sign up for article alerts and news from this journal
• Follow us on Twitter
• Follow us on Facebook

Who reads the journal?

Learn more about the impact the EURASIP Journal on Wireless Communications and Networking has worldwide

Annual Journal Metrics

2022 Citation Impact 2.6 - 2-year Impact Factor 2.5 - 5-year Impact Factor 1.098 - SNIP (Source Normalized Impact per Paper) 0.985 - SJR (SCImago Journal Rank)

2023 Speed 23 days submission to first editorial decision for all manuscripts (Median) 157 days submission to accept (Median)

2023 Usage  1,226,841 downloads 99 Altmetric mentions 

• More about our metrics

Affiliated with

Secure space–time-modulated millimetre-wave wireless links that are resilient to distributed eavesdropper attacks

What should 6G be?

Metasurface-enabled smart wireless attacks at the physical layer

Introduction.

Over the last two decades, wireless communications exploiting radio-frequency waves have become a ubiquitous feature of modern life. With each subsequent advance in technology has come countless new tools and capabilities, transforming the way we live. Now, as the rollout of 5G systems continues, researchers are considering the design of subsequent generations of networks, as well as visions for future implementations of Wi-Fi, Bluetooth, and other short-range wireless systems. It is worth noting that most previous wireless platforms, from the days of Marconi, have been confined to operate in the frequency range below a few gigahertz. Yet, rapid growth in demand for wireless services has changed the game; we are now forced to consider using higher frequencies, in order to find the bandwidth needed to support continued exponential growth in wireless traffic. One of the novel features of modern Wi-Fi and 5G variants such as IEEE 802.11ay 1 involves their ability to access higher frequencies in the millimeter-wave range, above 10 GHz. As these systems mature, it is therefore natural that research interests have now begun to turn to even higher frequencies. Like the mmWave Wi-Fi and 5G bands, the use of these higher frequencies is motivated in large part by the desire for access to larger bandwidth, and the associated higher data rates. Indeed, although the maximum data rate that can be supported within the 5G standard exceeds 7 gigabits per second (Gbps), more than an order of magnitude larger than the fastest 4G data rate, the huge (and ongoing) growth in demand for wireless access has made it clear that even higher rates will be needed in the future 2 .

For this reason, the cutting edge of wireless research lies at frequencies above 100 GHz 3 , often referred to as the “terahertz (THz) range”. Most of this research is focused on a few specific broad spectral bands, including the waveguide D band (110−170 GHz) which has been previously employed for television broadcasts during the Beijing Olympics 4 , and the higher frequency bands defined by the recent IEEE 802.15.3d standards document (252−322 GHz) 5 . These frequencies are well beyond even the highest millimeter-wave bands included in today’s Wi-Fi and 5G standards.

Opening this relatively unexplored realm of the electromagnetic spectrum will involve a host of challenging new research problems. In this Perspective article, we discuss some of the interesting issues facing researchers in the race to develop ultra-high-frequency wireless systems. Many of these challenges are associated with aspects of the physics of the interaction of these high-frequency waves with the world. Above 100 GHz, system designers will need to consider some physical regimes that have not previously been relevant for legacy wireless systems, or even in some cases for the mmWave bands of 5G. We first consider a few of the more prominent issues associated with these new operating regimes. We note that near-infrared or visible light optical communication systems, operating at even higher frequencies, are also of significant research interest, but are beyond the scope of this article.

Some challenges

One aspect of this discussion can be understood from the Friis equation, which describes the power received by an antenna P RX in a line-of-sight point-to-point wireless link. Expressed in dB, this relation is:

Here, P TX is the power generated by the transmitter, G T and G R are the transmitter and receiver antenna gains respectively (in dBi), and the last term, the free-space path loss (FSPL), describes the decrease in power per unit area of an expanding electromagnetic wavefront in terms of the propagation distance D and the wavelength λ 6 . This term becomes dominant at high frequencies. When considering an increase in the frequency by a factor of 100 (for example, from the typical 4G cellular frequency of 2.8 GHz to 280 GHz, a frequency in the 802.15.3d standard), the FSPL increases by 40 dB (see Fig.  1 ). Because the FSPL is smaller at lower frequencies, high-gain antennas are not always required; it is possible to operate a wireless link in which the transmitter broadcasts to a wide range of angles. For example, typical cellular antennas often span a 120° broadcast sector. At higher frequencies, the increasing FSPL can be offset with high-gain antennas, which concentrate the radiated power into a smaller angular range. Above 100 GHz, these broadcasts begin to act more like beams, propagating in a well-defined direction with low divergence 7 . There are of course many possible options for high-gain antennas, but translating these to the THz range is not always trivial, due to (among other things) the requirement of broadband operation. For example, phased array antennas are a well-established technology at lower frequencies, employing tuned phase shifters for each of the antenna elements in an array to implement beam steering or wavefront shaping. This approach, also being used in 5G systems, becomes more challenging as we design systems with larger fractional bandwidths. Phase shifters commonly operate at a fixed wavelength or frequency. When injecting a broadband signal to a phase shifter, the different frequency components experience different phases, resulting in beam squinting. Instead, a true-time-delay operation, in which all the signal frequency components experience the same phase delay, may be required in place of a simple phase shifter for individual elements of an antenna array 8 . The design of active efficient high-gain antennas with suitable form factors and efficiency remains an important research challenge.

The attenuation of a propagating radio wave due to both free-space path loss and atmospheric absorption, for an assumed propagation distance of 100 m, at a temperature of 15 °C and relative humidity of 59%, using a standard atmospheric model (see [10, 11]). The shaded areas indicate the range of frequencies corresponding to legacy wireless systems, the 5G millimeter-wave range, and the THz spectrum. The hatched areas are the two bands of significant interest for communications mentioned in the text.

A second important distinction between low and high-frequency propagation involves atmospheric attenuation (which has been neglected in Eq. ( 1 ) above). This loss also increases with frequency, in the form of several spectrally narrow absorption peaks riding on top of a smoothly increasing continuum absorption. In terrestrial systems, all of the important absorption lines above 120 GHz are due to rotational and ro-vibrational excitations in gas-phase water molecules 9 , 10 , some of which are strong enough to inhibit long-range propagation for frequencies near their line centers. These discrete absorption lines, therefore, serve to break the spectrum up into a series of broad bands which are well suited for transmission over longer distances, in which the relatively small continuum background (due to water dimers and other species) is the dominant contribution to atmospheric loss 11 . In fact, these absorption resonances need not always be considered a hindrance; with careful frequency tuning, they can be exploited for enhanced wireless security 12 . Despite some older conventional wisdom, the atmosphere is not opaque to radiation in the 100–1000 GHz range; if the H 2 O lines are avoided, point-to-point links in the km range are certainly feasible 4 , 13 , 14 , 15 . Inclement weather also contributes additional loss; 16 , 17 however, these may be tolerable under certain conditions, and indeed terahertz beams appear to be more robust against atmospheric scintillation and certain weather conditions (e.g., fog) than, for example, free-space optical signals in the near-infrared 18 .

A third issue of note is of the roles of scattering from surfaces and of material absorption. When considering interactions with surfaces, the characteristics of the scattered field are determined by the roughness of the surface, in comparison with the wavelength of the radiation, as well as the extent to which the surface absorbs (rather than scatters) the incoming radiation. A smooth (compared to lambda) surface reflects like a mirror; a rough surface produces a diffuse (not strongly directional) scattered wave. In a typical indoor environment, for instance, conventional wireless systems operate at frequencies where absorption is low in many materials, and where many surfaces are smooth compared to the (longer) wavelength. So, it is generally assumed that there can be many multiply-scattered paths between the transmitter and receiver, producing a rich scattering environment in which the field at any location is a stochastic superposition of many different wavelets. In contrast, the wireless channel at THz frequencies is quite different 19 . Typically a propagating wave experience much higher attenuation when interacting with most surfaces, due to absorption losses in the materials 20 , and commonly encountered surfaces can either be smooth or rough, in comparison with the (much smaller) wavelength (see Fig.  2 ). As a result, both indoor 21 and outdoor 22 environments are typically much more sparse, with fewer paths connecting the transmitter to receiver. Because many surfaces are smooth enough to act like mirrors, scattering in a specular direction (i.e., angle of reflection = angle of incidence) can often be dominant. Researchers have therefore been able to exploit ray tracing as an accurate means for predicting and understanding signal paths in THz propagation simulations 21 , 23 . In addition, due to the opacity of many objects including people, issues such as blockage of the direct line-of-sight (LOS) path can pose challenges for maintaining connectivity, as would be the case with a laser-based free-space optical link. However, due to the millimeter-scale wavelength, steering around such blockage events by exploiting a specular reflection from a surface in the environment is more feasible at THz frequencies 24 . Even non-specular reflections (diffuse scattering from rough surface 25 ) can be employed to maintain a link, although obviously with a lower signal-to-noise ratio 26 and added dispersion 27 . The shift from omnidirectional broadcasts with rich scattering to directional beams with sparse paths also has important implications for the security of such communication channels, rendering eavesdropping more challenging. Yet, vulnerabilities due to scattering still remain 28 , 29 , 30 , and must be considered in system design.

Measured bit error rate (BER) for a 2-m link which incorporates a specular reflection from a cinderblock wall, as shown in the top left photo. The effects of absorption and scattering are separated by measuring the link on the bare wall (blue points), the same wall with a conformal metal foil coating that eliminates penetration into the cinderblock (red points), and a flat metal plate which eliminates both absorption and scattering (black points). The photo images in ( a ) depict the three situations corresponding to the measurements in ( b ). Reprinted from Ma, J., Shrestha, R., Moeller, L. & Mittleman, D. M.; Channel performance of indoor and outdoor terahertz wireless links. APL Photon. 3, 051601 (2018)., with the permission of AIP Publishing.

Defining wavefronts and waveforms

These various novel features of THz waves force us to rethink common practices in wireless communication systems and, at the same time, open the door to new strategies not available in traditional wireless networks in microwave and even millimeter-wave bands.

On the one hand, relating to the spatial behavior of terahertz radiation, the requirement for high-gain directional antennas strongly suggests the use of radiating structures that are much larger than the wavelength. By recalling that the far field of an antenna occurs for distances greater than 2 × D 2 /λ, where D is the antenna’s largest dimension, it is likely that many wireless systems at terahertz frequencies will operate in the near field. For example, a 10 cm antenna, such as a dish antenna or an antenna array, at 130 GHz has a far-field distance of 8.6 m and the same antenna size at 300 GHz has a far-field distance of 20 m, larger than many indoor environments in which a THz LAN could be employed. This is a major distinction from lower frequency wireless systems, which generally operate exclusively in the far field.

This result has multiple consequences. First, wireless propagation, channel, and multi-user interference models, which have been derived under the assumption of far-field operation 6 , cannot simply be repurposed for higher frequency systems. Indeed, many models for terahertz communications continue to neglect to capture near-field effects 31 . Second, most algorithms behind the control of smart directional antenna systems, including beamforming and beam-steering, have also generally been developed under the far-field assumption 32 . For many possible antennas, including large radiating structures such as the increasingly popular intelligent surfaces 33 , this is not the case even at lower frequencies.

To overcome this latter challenge, there are several recent works 34 , 35 that explore beam focusing as a way to achieve beamforming-like capabilities but in the near field. In beam focusing, the weights or phases at different antenna elements are set to emulate that of a dielectric lens. While this is a valid solution for static scenarios, tracking and constantly changing the point on which the signal needs to be focused results in a significant overhead in terms of signaling the channel state information.

Going beyond beam focusing, if we are ready to abandon common practices and assumptions such as that the generated signal can be approximated as a plane wave or a Gaussian directional beam, operating in the near field opens the door to a host of new possibilities in wavefront engineering (whereby wavefronts we refer to the spatial intensity and phase profiles of the signals being transmitted). Although many of these ideas have been considered for some time, for instance in the optics community, it is only with the advent of directional links that they may reach their full potential in wireless systems. For example, at lower frequencies where received signals can often contain rich multi-path components, it can be challenging to exploit polarization diversity to double channel capacity. In contrast, such strategies are likely to be far more effective with a line-of-sight directional link 36 .

Other important examples may arise from considerations of more exotic wavefronts which can be prepared in the near field of an emitting aperture. For instance, by adopting Bessel beams, i.e., beams whose intensity profile in space can be described by a Bessel function of a certain order 37 , a beam can focus (in the near field) not at a point but along a line. This can drastically simplify the operational requirements in mobile networks. Moreover, Bessel beams exhibit a self-healing property, i.e., even when partially blocked by an obstacle, they can recreate the original intensity and phase profile at a distance. Similarly, the use of accelerating beams such as Airy beams, which can be programmed at the origin to bend after a given number of wavelengths 38 , can also be utilized to overcome or minimize the impact of obstacles, a major problem for practical mobile terahertz communications and sensing systems. Figure  3 shows computed cross-sections of a few of these options, illustrating the dramatically different behavior that can be obtained in the near field of a transmitting aperture.

Calculated electric field (left) and intensity (right) patterns for three engineered near-field radiation patterns: a focused Gaussian beam (top), a Bessel beam (middle), and an accelerating Airy beam (bottom).

Further, all these beams can also be engineered to carry orbital angular momentum (OAM). Different OAM mode orders are orthogonal, enabling the multiplexing of streams at the same frequency, at the same time, and in the same direction 39 . As discussed in the literature 40 , OAM multiplexing can be seen as a particular case of multiple input multiple output (MIMO) communications, but one in which channels are orthogonal from the start (and not because of how multi-path propagation affects them). Moreover, while these wavefronts can be generated using static phase masks (such as axicons for Bessel beams or spiral phase plates for different OAM modes), the same can be achieved by the utilization of dense antenna arrays 41 , 42 , which (unlike phase masks) could also in principle be dynamically reconfigurable 43 , 44 . We note that the security vulnerabilities associated with using such unusual wavefronts could be quite different from those associated with conventional side-lobe eavesdropping or jamming attacks 45 , and could offer new opportunities for enhancing link security 46 .

On the other hand, relating to the frequency behavior of terahertz radiation, there is a need for waveforms, the temporal variations of the transmitted signals, that can overcome various challenges, including those introduced by frequency-dependent molecular absorption in the channel (see Fig.  1 ) and by increasingly prominent hardware imperfections (e.g., nonlinearities in broadband frequency up- and down-converting systems). As of today, there is no answer to the question of what waveform will be used for 6G terahertz systems. While the common solutions at lower frequencies, including orthogonal frequency division modulation/multiplexing (OFDM), single-carrier OFDM also known as DFT spread OFDM, or the recently proposed orthogonal time-frequency-spatial modulation (OTFS) 47 could be adapted to terahertz frequencies, there are also other options, including waveforms unique to the terahertz band that enable applications not available at lower frequencies. For example, very short pulses, just a few hundreds of femtoseconds long, as in terahertz time-domain spectroscopy (THz-TDS) platforms 48 or BiCMOS impulse radiators 49 , can be utilized to implement low-complexity non-coherent modulation which is able to support a large number of users transmitting at very large data rates over a short range, provided that proper equalization techniques are implemented to compensate for the effects of multi-path propagation 2 , 50 . This is particularly useful when the encoding purposely biases the transmission of zeros over the transmission of ones to overcome the impact of noise and interference. At the same time, for longer communication distances, the broadening of the molecular absorption lines results in narrower communication bandwidths at longer distances. This effect can be exploited to use the channel as a filter and help to separate simultaneous data streams at the same frequency for users in the same direction but at different distances (see Fig.  4 ) 51 . Moreover, if spectral efficiency and peak data rates are not the drivers, there are other ways to exploit the available bandwidth above 100 GHz, for example in the form of secure communication and spectrum sharing techniques based on ultra-broadband spread spectrum 52 .

Leveraging the spectral filtering effect of atmospheric water vapor absorption resonances to implement hierarchical bandwidth modulation, in which nearby users can access the full bandwidth of the transmitted signal, while more distant users, whose channel bandwidth is narrower, only employ the smaller range at the center of the spectrum.

Ultimately, we envision that the spatial and spectral aspects of terahertz signals should not be considered separately, but instead, the spatial wavefront and temporal waveform should be jointly designed to optimize the performance of systems and unleash this spectrum. For example, as discussed above, different wavefronts are commonly generated with different types of phase masks which can be, in some cases, intrinsically narrowband. However, when trying to transmit ultra-broadband signals through such structures, the resulting wavefronts are far from ideal. To prevent this, frequency-selective pre-distortion of the waveforms being transmitted can be employed to ensure that the desired wavefront is produced over the entire bandwidth. This becomes even more important when producing complex wavefronts using arrays that approximate the lens response with a discrete (rather than continuous) pattern.

Components for the physical layer

Of course, the impact of the unique properties of terahertz radiation does not end with propagation, wavefronts, and waveforms. It will also influence the redesign of common devices in traditional systems for operation at higher frequencies and will open the door to novel hardware solutions that are not practical or even possible at lower frequency bands.

As one example, the small wavelength of terahertz waves leads to small fundamental resonant antennas (e.g., dipole, slots, or patches). When used individually, these antennas exhibit low effective areas resulting in the very high spreading losses discussed above. However, it is this small size of radiating structures that allows us to integrate very large numbers of antennas in a very small footprint. For example, in a 10 cm × 10 cm footprint, one could in principle integrate 200 × 200 (40,000!) dipole antennas at 300 GHz spaced λ/2 apart. The fabrication of such large on-chip arrays is a significant challenge, but rapid progress is being made 43 , 44 .

While such antennas or radiating elements can be envisioned, there are other components besides the antennas that would need to be integrated into the chip (potentially through 3D stacking), depending on the application that is needed. For example, a true-time delay controller per element would be needed to engineer the aforementioned broadband wavefronts. Moreover, if the goal were to develop transmitting or receiving antenna arrays that can support MIMO communications, each antenna would require a complete RF chain (i.e., a local oscillator, mixer, filter, amplifier, and data converter). Integrating such arrays is a major bottleneck with today’s electronic and photonic transceiver technologies due to their size, as well as packaging and thermal constraints. Arrays with element spacing greater than λ/2 produce far-field radiation patterns with grating lobes, which could be leveraged for multi-beam systems, but are otherwise not desirable. Instead, antenna array architectures aimed at minimizing the number of RF chains while minimally impacting the array capabilities have been proposed, such as the array-of-sub-arrays architecture 47 , in which separate RF chains drive separate subsets of fixed or only phase-controlled antenna elements 53 . Other solutions could be the adoption of signal processing techniques for sparse antenna arrays, which so far have generally been used only in the context of imaging 54 , 55 .

There are also a number of new technologies that only become available when operating at terahertz frequencies (or above). For instance, researchers have proposed the use of graphene to build plasmonic transceivers and antennas that intrinsically operate in the terahertz band 56 . Graphene, which supports the propagation of surface plasmon polariton (SPP) waves at terahertz frequencies and at room temperature, can be used (1) as a two-dimensional electron gas where plasma waves oscillations at terahertz frequencies occur 57 , (2) as a plasmonic waveguide where the properties of SPP waves can be electrically tuned 58 , and (3) as the active element of a nano-patch antenna, able to convert SPP waves into free-space electromagnetic waves 59 , all with devices that are significantly smaller than the free-space wavelength. While being sub-wavelength in size leads to low radiation efficiencies, this can be compensated through dense integration of the elements. Moreover, the sub-wavelength nature of each radiator also leads to negligible mutual coupling as long as elements are placed more than a plasmonic wavelength apart. From a signal processing perspective, being able to sample space with a resolution higher than λ/2 leads to both oversampling gain and the ability to engineer wavefronts (such as those noted above) with much higher accuracy than traditional λ/2-spaced arrays could ever support 60 .

The shift to higher frequencies also offers fascinating opportunities to engineer devices with advanced functionality, which are either impossible or impractical at lower frequencies. For example, recent research has focused on leaky-wave antennas, based on guided wave devices which incorporate a mechanism to permit some fraction of the guided wave to ‘leak’ out into free space. This leaked signal manifests a strong coupling between the frequency of the radiation and the direction in which it propagates. Leaky-wave antennas are neither new nor exclusive to the terahertz range 61 . However, the wavelength scale, and spectral bandwidth, of signals at these high frequencies means that such devices can operate in a unique regime of form factor and functionality, such that it is now plausible to consider new roles for these components in wireless systems. Leaky-wave components can be valuable for multi-frequency signal distribution (i.e., multiplexing) 62 and for sensing tasks such as the radar-like location of objects within a broadcast sector 63 . If both transmitter and receiver are equipped with a leaky-wave antenna, they can together provide a fast and efficient method for simultaneously determining both the angular location of a mobile receiver and its angular rotation relative to the transmitter 64 . Building on this approach, recent work has demonstrated arrays with true-time-delay elements to accomplish similar localization tasks 65 . One could even envision creating arrays of leaky-wave devices for enhanced wavefront control. This is another idea which has previously been considered at lower frequencies 66 , but which could find new possibilities in a different frequency regime.

The challenging propagation of terahertz waves and, in particular, the issue of blockage, motivates the consideration of strategies to improve reliability. As noted above (see Fig.  2 ), non-line-of-sight paths are available, even at these high frequencies, although they are sparse relative to what is typically encountered at lower frequencies. One interesting approach relies on the development of devices that can help us engineer not only the transmitter and receiver but also the propagation environment (i.e., the channel). This idea has inspired a great deal of research in the general area of intelligent reflecting surfaces, which could be distributed throughout an indoor network to facilitate signal distribution and overcome transient blockage events. As with other devices discussed here, an intelligent reflecting surface (IRS), such as those based on programmable reflectarrays, has been considered previously at lower frequencies 67 . However, at frequencies above 100 GHz and with current applications in mind, the benefits that such structures bring to wireless systems may now prove too valuable to ignore. Today, there are numerous different technologies under consideration as the basis for IRS devices. For example, smart surfaces have been proposed which replace conventional switching elements employed at lower frequencies, such as varactor diodes, with graphene patches 56 . Dense reflectarrays with integrated switching elements have been designed and implemented in silicon CMOS 43 , 44 and in III-V semiconductor platforms using high-electron-mobility transistor (HEMT) structures 68 . We have also recently shown that array devices, in the hands of a clever adversary, can also open up interesting new security vulnerabilities (see Fig.  5 ) 69 . It is too early to tell how this interesting approach to engineering the broadcast environment will ultimately be achieved, but it is quite clear that any of these possible solutions would drastically change the way that networks are designed and operated.

A clever eavesdropper (Eve) can insert an engineered reflector, such as a flexible metasurface (photo) into the line-of-sight path between the transmitter (Alice) and the intended receiver (Bob), in order to direct a portion of the spectrum towards the eavesdropper (upper schematic). This low-profile attack would be difficult for Alice and Bob to detect, but would direct a significant signal toward Eve (lower panel).  Adapted from Zhambyl Shaikhanov, Fahid Hassan, Hichem Guerboukha, Daniel Mittleman, and Edward Knightly. 2022. Metasurface-in-the-middle attack: From Theory to Experiment. In Proceedings of the 15th ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec '22).© Association for Computing Machinery, New York, NY, USA, 257–267. https://doi.org/10.1145/3507657.3528549 .

Implications for the control plane

With the above considerations in mind, it becomes clear that networks of the future, which have the capability to exploit THz frequency bands, will operate quite differently from networks of today. One obvious example is that a transmitter, employing a narrow pencil-like beam, will need to know where to point it. This and other examples suggest that networks will require joint communications and sensing capabilities, and moreover that new approaches will be required for managing these capabilities in order to ensure high quality of service and efficient use of system resources.

As a first step, we note that radio sensing can have two purposes: The first purpose is to identify clients, devices, and objects in the environment, e.g., for presence detection and analysis of environmental objects and their mobility to optimize the signal-to-noise of wireless links 70 . The second purpose builds on the first and targets to understand the RF environment for network optimization, e.g., to localize uncontrolled sources of interference in order to avoid or null them. Today’s RF sensing applications are quite impressive and include monitoring people in a room behind a wall 71 and monitoring individual heart rate 72 . Unfortunately, today’s RF methods have two fundamental limits. First, their inputs are the gains and phases of the channel matrix H and they subsequently rely on the dimensionality of H for resolution. Thus, to improve resolution further, array sizes would need to approach a massive MIMO scale, thus incurring the corresponding issues of size, cost, power consumption, and computational requirements. Second, because these methods were designed to operate below 6 GHz, their wavelength is centimeter to decimeter scale, limiting resolution correspondingly 73 .

As noted above, the use of THz frequencies opens up a number of new possibilities for joint sensing and communications, with important implications for the functioning of the control plane of the network, which is responsible for functions such as beam alignment and spectrum management. For example, as mentioned above, a directive transmitter and receiver must dynamically align their beams toward each other. In today’s standards for both 5G and Wi-Fi, a serial sector sweep is used for initial beam alignment, to sequentially test different directions. This trial-and-error method becomes increasingly cumbersome as beams become narrower. In contrast, the aforementioned leaky-wave device can be used to rapidly track mobile clients by using the received spectral signature 64 to estimate the receiver’s relative angle from the transmitter (see Fig.  6 ), a scheme which can be generalized to three-dimensional localization 74 . As another example, with arrays of sub-wavelength elements, one can envision a centimeter-scale surface with ~1000 independently controllable devices. This high oversampling yields new possibilities for dual-purposing communication and sensing: not only could one realize classical communication capabilities (e.g., beamforming and nulling of interferers or enhancing security), but one could also realize sensor functions (e.g., localization of users) with the same device 75 .

An integrated circuit, fabricated in silicon CMOS, which realizes a leaky-wave antenna for single-shot localization of multiple users in a broadcast sector via broadband excitation of the angularly dispersive aperture. Figure adapted from H. Saeidi, S. Venkatesh, X. Lu and K. Sengupta, "THz Prism: One-Shot Simultaneous Localization of Multiple Wireless Nodes With Leaky-Wave THz Antennas and Transceivers in CMOS," in IEEE Journal of Solid-State Circuits, vol. 56, no. 12, pp. 3840-3854, Dec. 2021, https://doi.org/10.1109/JSSC.2021.3115407 . with permission of the authors under a Creative Commons license: https://creativecommons.org/licenses/by/4.0 .

Likewise, beam steering must also incorporate cases in which a direct line-of-sight path is not available. As discussed above, an IRS could be used to realize a reflected path thereby increasing coverage and avoiding blockage. However, building the device is not enough; in order to function properly, the network’s control plane must discover and configure this path, including properties of the IRS itself: for example, if the IRS provides a non-specular reflection, it must know the targeted incoming and outgoing angles. Since a network may alternate serving users in time, the IRS would need to reconfigure not only due to user mobility, but also according to which users are transmitting and receiving. Beam steering devices must also consider the new wavefronts described in Section 3. For example, in typical demonstrations of beams such as OAM 39 , the transmitter, and receiver are manually aligned and are typically placed broadside. To employ such wavefronts in a mobile network will require adaptation not only for location, but also for the relative orientations of the transmitter and receiver when they are not ideally oriented.

As noted above, the aforementioned techniques based on the idea of an IRS have previously been considered for use at lower frequencies, but their implementation takes on new urgency at these higher frequencies. In addition, there are some approaches which have been more widely employed at lower frequencies, and which can also offer valuable capabilities in the THz range. One good illustration is the assessment of angle-of-arrival for a mobile user via cooperative estimation of spatial correlations among multiple antennas, for example in a massive MIMO architecture. This possibility has recently been considered by Peng et al. 76 in the context of a THz network. Such legacy control-plane methods can play an important role, but will in general need some rethinking in view of the highly directional nature of transmissions in these networks, as well as the possibility (discussed above) that the user could be in the near field of the array.

Today’s wireless networks provide multi-user capabilities, in which an access point or base station transmits to (or receives from) multiple users simultaneously in order to increase aggregate data rate and decrease latency. Realizing this capability with THz frequencies will require two new advances. First, waveforms and modulation formats must be designed to support simultaneous transmission, incorporating that users will not be co-located and will be using directional transmissions. In some downlink cases, spatial separation of receivers combined with narrow beams may provide a simple starting point. Yet, for the uplink, and even for the downlink when users are close together, interference and co-stream management must be carefully controlled. Second, even if a network has the physical capability to realize a multi-user transmission, control plane mechanisms are needed to coordinate the transmission. Namely, the control plane must identify and localize the users, determine the appropriate spectrum and modulations to use, trigger the transmission at the correct time, and so on. In some cases, these functions are sufficiently similar to those of existing networks that comparable methods can be used; in other cases, entirely new protocols will need to be developed. For example, while traditionally medium access control protocols are driven by the transmitter, an alternative procedure based on receiver-initiated link synchronization, in which a receiver periodically polls potential transmitters as its antenna sweeps / scans the space, can increase the reliability and throughput and reduce latency 77 .

The many challenges discussed in this article have inspired a great deal of research over the last few years (only a small fraction of which could be included here). One challenge, not discussed above, involves the potential interference of wireless signals at these frequencies with existing (for the most part, passive) users involved in earth sensing or radio astronomy. Numerous research communities employ highly sensitive receivers to harvest information about the status of our atmosphere and the molecular composition of astronomical objects. It is critical that any active communication services that exploit frequencies above 100 GHz must be designed to avoid interference with these important existing communities 78 . Of course, because of the higher atmospheric attenuation and the high gain of transmitting antennas, issues of sharing and interference may be quite different at these high frequencies. More research is required, for example, to establish the limits for interference, or to demonstrate antenna configurations whose side lobes are designed to avoid interference with overhead satellites.

Unsurprisingly, the daunting nature of the technical challenges has also inspired some skepticism from some researchers in the field. A few have noted that R&D expenditures in THz systems from many of the major telecommunications companies remain only a small fraction of their total R&D budget. Of course, this is not surprising, since the market for these systems also remains tiny. At this juncture, one should not expect massive private sector investment in a technology that is probably at least a decade away. Another oft-stated concern relates to the need for such systems. Twenty years ago, a common refrain was that nobody would ever require frequency bands above 10 GHz for consumer applications; ten years ago, it was 60 GHz; today, with the emergence of the first commercial backhaul devices operating in D band 79 , the threshold has now moved to 140 GHz. In fact, we choose to regard this moving target as an optimistic indicator of the rapid progress in the field. This progress is embodied in exciting recent publications, including breakthrough new link demonstrations 80 , 81 and rapid advances in solid-state device technology 82 , 83 , 84 , 85 .

Of course, there are valid reasons for concern; the challenges discussed in this article are indeed formidable. Nevertheless, we feel that the research results of the last few years have established that THz technologies are a promising foundation for future needs in wireless networks, which seem likely to exploit these frequencies for at least some of their key functions 86 . While many open questions remain, there is at this point a clear and compelling motivation to pursue the goal of THz wireless.

IEEE 802.11 Working Group. Enhancements for very high throughput for operation in license-exempt bands above 45 GHz. IEEE P802.11ay/D3.0 (2019).

Kürner, T., Mittleman, D. M. & Nagatsuma, T. THz Communications: Paving the Way towards Wireless Tbps (Springer, 2021).

Shafie, A. et al. Terahertz communications for 6G and beyond wireless networks: challenges, key advancements, and opportunities. IEEE Network (IEEE, 2022).

Hirata, A. et al. 120-GHz-band wireless link technologies for outdoor 10-Gbit/s data transmission. IEEE Trans. Microw. Theory Tech. 60 , 881–895 (2012).

Article   ADS   Google Scholar  

Petrov, V., Kürner, T. & Hosako, I. First standardization efforts for sub-terahertz band communications towards 6G. IEEE Commun. Mag. 58 , 28–33 (2020).

Article   Google Scholar  

Balanis, C. A. Antenna Theory: Analysis and Design 4th ed. (John Wiley & Sons, 2016).

Kleine-Ostmann, T. & Nagatsuma, T. A review on terahertz communications research. J. Infrared Millim. Terahertz Waves 32 , 143–171 (2011).

Fu, Z. & Chen, R. T. Five-bit substrate guided wave true-time delay module working up to 2.4 Thz with a packing density of 2.5 lines/cm2 for phased array antenna applications. Opt. Eng. 6 , 1838–1844 (1998).

International Telecommunication Union, Radiocommunication Sector (ITU-R), Recommendation P.676-11, Attenuation by atmospheric gases. https://www.itu.int/rec/R-REC-P.676-11-201609-I (2016).

O’Hara, J. F. & Grischkowsky, D. R. Comment on the veracity of the ITU-R recommendation for atmospheric attenuation at terahertz frequencies. IEEE Trans. Terahertz Sci. Technol. 8 , 372–375 (2018).

Yang, Y., Mandehgar, M. & Grischkowsky, D. THz-TDS characterization of the digital communication channels of the atmosphere and the enabled applications. J. Infrared Millim. Terahertz Waves 36 , 97–129 (2015).

Fang, Z., Hornbuckle, M. & Mittleman, D. M. Secure communication channels using atmosphere-limited line-of-sight terahertz links. IEEE Trans. Terahertz Sci. Technol. 12 , 363–369 (2022).

Kallfass, I. et al. 64 Gbit/s Transmission over 850 m Fixed Wireless Link at 240 GHz Carrier Frequency. J. Infrared Millim. Terahertz Waves 36 , 221–233 (2015).

Wu, Q. et al. A 21 km 5 Gbps real time wireless communication system at 0.14 THz. In 42nd International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz) (IEEE, 2017).

Sen, P., Siles, J. V., Thawdar, N. & Jornet, J. M. Multi-kilometer multi-gigabit-per-second (sub) terahertz communications. Nat. Electron. https://doi.org/10.1038/s41928-022-00897-6 (2022).

Federici, J. F., Ma, J. & Moeller, L. Review of weather impact on outdoor terahertz wireless communication links. Nano Commun. Netw. 10 , 13–26 (2016).

Sen, P. et al. Terahertz communications can work in rain and snow: Impact of adverse weather conditions on channels at 140 GHz. In 6th ACM Workshop on Millimeter-Wave and Terahertz Networks and Sensing Systems 13–18 (ACM, 2022).

Su, K., Moeller, L., Barat, R. B. & Federici, J. F. Experimental comparison of performance degradation from terahertz and infrared wireless links in fog. J. Opt. Soc. Am. A 29 , 179–184 (2012).

Han, C. et al. Terahertz wireless channels: a holistic survey on measurement, modeling, and analysis. IEEE Commun. Surv. Tutor. 24 , 1670–1707 (2022).

De Beelde, B. et al. Material characterization and radio channel modeling at D-band frequencies. IEEE Access 9 , 153528–153539 (2021).

Priebe, S., Kannicht, M., Jacob, M. & Kürner, T. Ultra broadband indoor channel measurements and calibrated ray tracing propagation modeling at THz frequencies. J. Commun. Netw. 15 , 547–558 (2013).

Abbasi, N. A. et al. THz band channel measurements and statistical modeling for urban D2D environments. IEEE Trans. Wirel. Commun. https://doi.org/10.1109/TWC.2022.3184929 (2022).

Sheikh, F., El-Hadidy, M. & Kaiser, T. Terahertz band: Indoor ray-tracing channel model considering atmospheric attenuation. In IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting 1782-1783 (IEEE, 2015).

Ma, J., Shrestha, R., Moeller, L. & Mittleman, D. M. Channel performance of indoor and outdoor terahertz wireless links. APL Photon 3 , 051601 (2018).

Jansen, C. et al. Diffuse scattering from rough surfaces in THz communication channels. IEEE Trans. Terahertz Sci. Tech. 1 , 462–472 (2011).

Ma, J., Shrestha, R., Zhang, W., Moeller, L. & Mittleman, D. M. Terahertz wireless links using diffuse scattering from rough surfaces. IEEE Trans. Terahertz Sci. Tech. 9 , 463–470 (2019).

Article   ADS   CAS   Google Scholar  

Messenger, R., Strecker, K., Ekin, S. & O’Hara, J. F. Dispersion from diffuse reflectors and its effect on terahertz wireless communication performance. IEEE Trans. Terahertz Sci. Tech. 11 , 695–703 (2021).

Ma, J. et al. Security and eavesdropping in terahertz wireless links. Nature 563 , 89–93 (2018).

Article   ADS   CAS   PubMed   Google Scholar  

Mei, Y., Ma, Y., Ma, J., Moeller, L. & Federici, J. F. Eavesdropping risk evaluation on terahertz wireless channels in atmospheric turbulence. IEEE Access 9 , 101916–101923 (2021).

Herold, C., Doeker, T., Eckhardt, J. M. & Kürner, T. Investigation of eavesdropping opportunities in a meeting room scenario for THz communications. In 16th European Conference on Antennas and Propagation (EuCAP) (IEEE, 2022).

Ju, S. & Rappaport, T. S. Sub-terahertz spatial statistical MIMO channel model for urban microcells at 142 GHz. In IEEE Global Communications Conference ( GLOBECOM ) (IEEE, 2021).

Toskala, A., Holma, H. & Nakamura, T. 5G Technology: 3GPP New Radio (John Wiley & Sons, 2020).

Wu, Q., Zhang, S., Zheng, B., You, C. & Zhang, R. Intelligent reflecting surface-aided wireless communications: a tutorial. IEEE Trans. Commun. 69 , 3313–3351 (2021).

Rouhi, K., Hosseininejad, S. E., Abadal, S., Khalily, M. & Tafazolli, R. Multi-channel near-field terahertz communications using reprogrammable graphene-based digital metasurface. J. Lightwave Technol. 39 , 6893–6907 (2021).

Zhang, H. et al. Beam focusing for near-field multiuser MIMO communications. IEEE Trans. Wirel. Commun. 21 , 7476–7490 (2022).

Jo, O., Kim, J.-J., Yoon, J., Choi, D. & Hong, W. Exploitation of dual-polarization diversity for 5G millimeter-wave MIMO beamforming systems. IEEE Trans. Antennas Propag. 65 , 6646–6655 (2017).

Durnin, J., Miceli, J. J. & Eberly, J. H. Comparison of Bessel and Gaussian beams. Opt. Lett. 13 , 79–80 (1988).

Efremidis, N. K., Chen, Z., Segev, M. & Christodoulides, D. N. Airy beams and accelerating waves: an overview of recent advances. Optica 6 , 686–701 (2019).

Zhou, H. et al. Utilizing multiplexing of structured THz beams carrying orbital-angular-momentum for high-capacity communications. Opt. Express 30 , 25418–25432 (2022).

Edfors, O. & Johansson, A. J. Is orbital angular momentum (OAM) based radio communication an unexploited area? IEEE Trans. Antennas Propag. 60 , 1126–1131 (2012).

Article   ADS   MathSciNet   MATH   Google Scholar  

Li, J.-S. & Chen, J.-Z. Multi-beam and multi-mode orbital angular momentum by utilizing a single metasurface. Opt. Express 29 , 27332–27339 (2021).

Article   ADS   PubMed   Google Scholar  

Khan, M. I. W. et al. A 0.31-THz orbital-angular-momentum (OAM) wave transceiver in CMOS with bits-to-OAM mode mapping. IEEE J. Solid State Circuits 57 , 1344–1357 (2021).

Venkatesh, S., Lu, X., Saeidi, H. & Sengupta, K. A high-speed programmable and scalable terahertz holographic metasurface based on tiled CMOS chips. Nat. Electron 3 , 785–793 (2020).

Article   CAS   Google Scholar  

Monroe, N. M. et al. Electronic THz pencil beam forming and 2D steering for high angular-resolution operation: a 98×98 unit, 265 GHz CMOS reflectarray with in-unit digital beam shaping and squint correction. In IEEE International Solid-State Circuits Conference ( ISSCC ) (2022).

Shrestha, R., Guerboukha, H., Fang, Z., Knightly, E. & Mittleman, D. M. Jamming a terahertz wireless link. Nat. Commun. 13 , 3045 (2022).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Woo, J. et al. Physical-layer security for THz communications via orbital angular momentum waves. In IEEE Workshop on Signal Processing Systems ( SiPS ) (IEEE, 2022).

Hadani, R. et al. Orthogonal time frequency space (OTFS) modulation for millimeter-wave communications systems. In International Microwave Symposium ( IMS ) 681–683 (IEEE, 2017).

Grischkowsky, D., Keiding, S., Exter, M. V. & Fattinger, C. Far-infrared time-domain spectroscopy with terahertz beams of dielectrics and semiconductors. J. Opt. Soc. Am. B 7 , 2006–2015 (1990).

Chen, P., Wang, Y. & Babakhani, A. A 4 ps amplitude reconfigurable impulse radiator with THz-TDS characterization method in 0.13 μm SiGe BiCMOS. In IEEE MTT-S International Microwave Symposium ( IMS ) (IEEE, 2016).

Jornet, J. M. & Akyildiz, I. F. Femtosecond-long pulse-based modulation for terahertz band communication in nanonetworks. IEEE Trans. Commun. 62 , 1742–1754 (2014).

Bodet, D., Sen, P., Hossain, Z., Thawdar, N. & Jornet, J. M. Hierarchical bandwidth modulations for ultra-broadband communications in the terahertz band. IEEE Trans. Wireless Commun . (2022).

Bosso, C. et al. Ultrabroadband spread spectrum techniques for secure dynamic spectrum sharing above 100 GHz between active and passive users. In International Symposium on Dynamic Spectrum Access Networks ( DySPAN ) 45–52 (IEEE, 2021).

Abu-Surra, S. et al. End-to-end 6 G terahertz wireless platform with adaptive transmit and receive beamforming. in IEEE International Conference on Communications Workshops ( ICC Workshops ) 897–903 (IEEE, 2022).

Watts, C. M. et al. Terahertz compressive imaging with metamaterial spatial light modulators. Nat. Photon 8 , 605–609 (2014).

Sun, C., Chang, Q., Zhao, R., Wang, Y. & Wang, J. Terahertz imaging based on sparse MIMO array. In International Conference on Microwave and Millimeter Wave Technology ( ICMMT ) (IEEE, 2020).

Singh, A., Andrello, M., Einarsson, E., Thawdar, N. & Jornet, J. M. Design and operation of a smart graphene–metal hybrid reflectarray at THz frequencies. In 14th European Conference on Antennas and Propagation ( EuCAP ) (IEEE, 2020).

Bandurin, D. A. et al. Resonant terahertz detection using graphene plasmons. Nat. Commun. 9 , 5392 (2018).

Tu, N. H. et al. Active spatial control of terahertz plasmons in graphene. Commun. Mater. 1 , 7 (2020).

Jornet, J. M. & Akyildiz, I. F. Graphene-based plasmonic nano-antenna for terahertz band communication in nanonetworks. IEEE J. Sel. Areas Commun. 31 , 685–694 (2013).

Yeang, C.-P., Wornell, G. W. & Zheng, L. Oversampling transmit and receive antenna arrays. In International Conference on Acoustics, Speech and Signal Processing 2522–2525 (IEEE, 2010).

Jackson, D. R., Caloz, C. & Itoh, T. Leaky wave antennas. Proc. IEEE 100 , 2194–2206 (2012).

Ma, J., Karl, N. J., Bretin, S., Ducournau, G. & Mittleman, D. M. Frequency-division multiplexer and demultiplexer for terahertz wireless links. Nat. Commun. 8 , 729 (2017).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Matsumoto, H., Watanabe, I., Kasamatsu, A. & Monnai, Y. Integrated terahertz radar based on leaky-wave coherence tomography. Nat. Electron 3 , 122–129 (2020).

Ghasempour, Y., Shrestha, R., Charous, A., Knightly, E. & Mittleman, D. M. Single-shot link discovery for terahertz wireless networks. Nat. Commun. 11 , 2017 (2020).

Li, R., Yan, H. & Cabric, D. Rainbow-link: beam-alignment-free and grant-free mmW multiple access using true-time-delay array. IEEE J. Sel. Areas Commun. 40 , 1692–1705 (2022).

Nguyen, H. V., Abielmona, S., Rennings, A. & Caloz, C. Pencil-beam full-space scanning 2D CRLH leaky-wave antenna array. In International Symposium on Signals, Systems and Electronics 139–142 (IEEE, 2007).

Kamoda, H., Iwasaki, T., Tsumochi, J., Kuki, T. & Hashimoto, O. 60-GHz electronically reconfigurable large reflectarray using single-bit phase shifters. IEEE Trans. Antennas Propag. 59 , 2524–2531 (2011).

Zhao, Y. et al. High-speed efficient terahertz modulation based on tunable collective-individual state conversion within an active 3 nm two-dimensional electron gas metasurface. Nano Lett. 19 , 7588–7597 (2019).

Shaikhanov, Z., Hassan, F., Guerboukha, H., Mittleman, D. & Knightly, E. Metasurface-in-the-middle attack: from theory to experiment. In 15th ACM Conference on Security and Privacy in Wireless and Mobile Networks ( WiSec ‘22 ) 257–267 (ACM, 2022).

Kanhere, O. & Rappaport, T. S. Outdoor sub-THz position location and tracking using field measurements at 142 GHz. In IEEE International Conference on Communications ( ICC ) (IEEE, 2021).

Zhao, M. et al. Through-wall human mesh recovery using radio signals. In IEEE/CVF International Conference on Computer Vision ( ICCV ) 10112–10121 (IEEE, 2019).

Lin, F. et al. Cardiac Scan: A non-contact and continuous heart-based user authentication system. In 23rd Annual International Conference on Mobile Computing and Networking ( MobiCom ‘17 ) 315–328 (ACM, 2017).

Kanhere, O. & Rappaport, T. S. Position location for futuristic cellular communications: 5G and beyond. IEEE Commun. Mag. 59 , 70–75 (2021).

Saeidi, H., Venkatesh, S., Lu, X. & Sengupta, K. THz prism: one shot simultaneous localization of multiple wireless nodes with leaky-wave THz antennas and transceivers in CMOS. IEEE J. Solid-State Circuits 56 , 3840–3854 (2021).

Domae, B. W., Boljanovic, V., Li, R. & Cabric, D. Machine learning prediction for phase-less millimeter-wave beam tracking. In IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication ( SPAWC ) (IEEE, 2022). https://doi.org/10.1109/SPAWC51304.2022.9833935

Peng, B., Guan, K. & Kürner, T. Cooperative dynamic angle of arrival estimation considering space–time correlations for terahertz communications. IEEE Trans. Wirel. Commun. 17 , 6029–6041 (2018).

Xia, Q., Hossain, Z., Medley, M. & Jornet, J. M. Link-layer synchronization and medium access control protocol for terahertz-band communication networks. IEEE Trans. Mob. Comput. 20 , 2–18 (2021).

Xing, Y. & Rappaport, T. S. Terahertz wireless communications: co-sharing for terrestrial and satellite systems above 100 GHz. IEEE Commun. Lett. 25 , 3156–3160 (2021).

Singh, A. et al. A D-band radio-on-glass module for spectrally-efficient and low-cost wireless backhaul. In IEEE Radio Frequency Integrated Circuits Symposium ( RFIC ) 99–102 (IEEE, 2020).

Kürner, T. & Kawanishi, T. Demonstrating 300 GHz wireless backhaul links – The ThoR approach. In 47th International Conference on Infrared, Millimeter and Terahertz Waves ( IRMMW-THz ) (IEEE, 2022).

Maes, D. et al. UTC photodiodes on silicon nitride enabling 100 Gbit/s Terahertz links at 300 GHz. In European Conference on Optical Communication ( ECOC ) (IEEE, 2022).

Rodwell, M. et al. Transistors for 100-300 GHz wireless. In IEEE 51st European Solid-State Device Research Conference ( ESSDERC ) (IEEE, 2021).

Wang, C. & Rebeiz, G. A 2-channel 136-156 GHz dual down-conversion I/Q receiver with 30 dB gain and 9.5 dB NF using CMOS 22 nm FDSOI. In IEEE Radio Frequency Integrated Circuits Symposium ( RFIC ) (IEEE, 2021).

Chen, Z., Choi, W. & O, K. K. 300-GHz double-balanced up-converter using asymmetric MOS varactors in 65-nm CMOS. IEEE J. Solid-State Circuits 57 , 2336–2347 (2022).

Mehta, Y., Thomas, S. & Babakhani, A. A 140–220-GHz low-noise amplifier with 6-dB minimum noise figure and 80-GHz bandwidth in 130-nm SiGe BiCMOS. IEEE Microwave Wireless Comp. Lett . 33 , 200–203 (2022).

Pang, X. et al. Bridging the terahertz gap: photonics-assisted free-space communications from the submillimeter-wave to the mid-infrared. J. Lightwave Technol. 40 , 3149–3162 (2022).

Download references

Acknowledgements

The authors gratefully acknowledge the assistance of Kaushik Sengupta, Hichem Guerboukha, and Duschia Bodet in assembling figures for this manuscript. D.M.M. acknowledges funding support from the US National Science Foundation (grant numbers 1923733, 1954780, 2148132, and 2211616) and the Air Force Research Laboratory (award number FA8750-19-1-0500). E.W.K. acknowledges funding support from Cisco, Intel, the US National Science Foundation (grant numbers 1955075, 1923782, 1824529, and 2148132), and the Army Research Laboratory (grant W911NF-19-2-0269). J.M.J. acknowledges funding support from the US National Science Foundation (grant numbers 1955004, 2011411, and 2117814) and the Air Force Research Laboratory (award number FA8750-19-1-0200).

Author information

Authors and affiliations.

Department of Electrical and Computer Engineering, Institute for the Wireless Internet of Things, Northeastern University, 360 Huntington Avenue, Boston, MA, 02115, USA

Josep M. Jornet

Department of Electrical & Computer Engineering, Rice University, 6100 Main St., Houston, TX, 77005, USA

Edward W. Knightly

School of Engineering, Brown University, 184 Hope St., Providence, RI, 02912, USA

• Daniel M. Mittleman

You can also search for this author in PubMed   Google Scholar

Contributions

D.M.M., J. M.J., and E.W.K. contributed equally to the writing of this manuscript.

Corresponding author

Correspondence to Daniel M. Mittleman .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Communications thanks Robert Elschner, Shuai Nie and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Jornet, J.M., Knightly, E.W. & Mittleman, D.M. Wireless communications sensing and security above 100 GHz. Nat Commun 14 , 841 (2023). https://doi.org/10.1038/s41467-023-36621-x

Download citation

Received : 29 September 2022

Accepted : 09 February 2023

Published : 15 February 2023

DOI : https://doi.org/10.1038/s41467-023-36621-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Ultra-compact quasi-true time delay for boosting wireless channel capacity.

• Bala Govind
• Thomas Tapen
• Alyssa Apsel

Nature (2024)

Curving THz wireless data links around obstacles

• Hichem Guerboukha

Communications Engineering (2024)

Impact of noise on data routing in flying Ad hoc networks

• Mohamed Ben Bezziane
• Bouziane Brik
• Amina Ben Bezziane

Optical and Quantum Electronics (2024)

A compact SiGe D-band power amplifier for scalable photonic-enabled phased antenna arrays

• Reinier Broucke
• Nishant Singh

Scientific Reports (2023)

Propagation of THz radiation in air over a broad range of atmospheric temperature and humidity conditions

• Fatima Taleb
• Mariana Alfaro-Gomez
• Enrique Castro-Camus

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Sensors (Basel)

Study and Investigation on 5G Technology: A Systematic Review

Ramraj dangi.

1 School of Computing Science and Engineering, VIT University Bhopal, Bhopal 466114, India; [email protected] (R.D.); [email protected] (P.L.)

Praveen Lalwani

Gaurav choudhary.

2 Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark; moc.liamg@7777yrahduohcvaruag

3 Department of Information Security Engineering, Soonchunhyang University, Asan-si 31538, Korea

Giovanni Pau

4 Faculty of Engineering and Architecture, Kore University of Enna, 94100 Enna, Italy; [email protected]

Associated Data

Not applicable.

In wireless communication, Fifth Generation (5G) Technology is a recent generation of mobile networks. In this paper, evaluations in the field of mobile communication technology are presented. In each evolution, multiple challenges were faced that were captured with the help of next-generation mobile networks. Among all the previously existing mobile networks, 5G provides a high-speed internet facility, anytime, anywhere, for everyone. 5G is slightly different due to its novel features such as interconnecting people, controlling devices, objects, and machines. 5G mobile system will bring diverse levels of performance and capability, which will serve as new user experiences and connect new enterprises. Therefore, it is essential to know where the enterprise can utilize the benefits of 5G. In this research article, it was observed that extensive research and analysis unfolds different aspects, namely, millimeter wave (mmWave), massive multiple-input and multiple-output (Massive-MIMO), small cell, mobile edge computing (MEC), beamforming, different antenna technology, etc. This article’s main aim is to highlight some of the most recent enhancements made towards the 5G mobile system and discuss its future research objectives.

1. Introduction

Most recently, in three decades, rapid growth was marked in the field of wireless communication concerning the transition of 1G to 4G [ 1 , 2 ]. The main motto behind this research was the requirements of high bandwidth and very low latency. 5G provides a high data rate, improved quality of service (QoS), low-latency, high coverage, high reliability, and economically affordable services. 5G delivers services categorized into three categories: (1) Extreme mobile broadband (eMBB). It is a nonstandalone architecture that offers high-speed internet connectivity, greater bandwidth, moderate latency, UltraHD streaming videos, virtual reality and augmented reality (AR/VR) media, and many more. (2) Massive machine type communication (eMTC), 3GPP releases it in its 13th specification. It provides long-range and broadband machine-type communication at a very cost-effective price with less power consumption. eMTC brings a high data rate service, low power, extended coverage via less device complexity through mobile carriers for IoT applications. (3) ultra-reliable low latency communication (URLLC) offers low-latency and ultra-high reliability, rich quality of service (QoS), which is not possible with traditional mobile network architecture. URLLC is designed for on-demand real-time interaction such as remote surgery, vehicle to vehicle (V2V) communication, industry 4.0, smart grids, intelligent transport system, etc. [ 3 ].

1.1. Evolution from 1G to 5G

First generation (1G): 1G cell phone was launched between the 1970s and 80s, based on analog technology, which works just like a landline phone. It suffers in various ways, such as poor battery life, voice quality, and dropped calls. In 1G, the maximum achievable speed was 2.4 Kbps.

Second Generation (2G): In 2G, the first digital system was offered in 1991, providing improved mobile voice communication over 1G. In addition, Code-Division Multiple Access (CDMA) and Global System for Mobile (GSM) concepts were also discussed. In 2G, the maximum achievable speed was 1 Mpbs.

Third Generation (3G): When technology ventured from 2G GSM frameworks into 3G universal mobile telecommunication system (UMTS) framework, users encountered higher system speed and quicker download speed making constant video calls. 3G was the first mobile broadband system that was formed to provide the voice with some multimedia. The technology behind 3G was high-speed packet access (HSPA/HSPA+). 3G used MIMO for multiplying the power of the wireless network, and it also used packet switching for fast data transmission.

Fourth Generation (4G): It is purely mobile broadband standard. In digital mobile communication, it was observed information rate that upgraded from 20 to 60 Mbps in 4G [ 4 ]. It works on LTE and WiMAX technologies, as well as provides wider bandwidth up to 100 Mhz. It was launched in 2010.

Fourth Generation LTE-A (4.5G): It is an advanced version of standard 4G LTE. LTE-A uses MIMO technology to combine multiple antennas for both transmitters as well as a receiver. Using MIMO, multiple signals and multiple antennas can work simultaneously, making LTE-A three times faster than standard 4G. LTE-A offered an improved system limit, decreased deferral in the application server, access triple traffic (Data, Voice, and Video) wirelessly at any time anywhere in the world.LTE-A delivers speeds of over 42 Mbps and up to 90 Mbps.

Fifth Generation (5G): 5G is a pillar of digital transformation; it is a real improvement on all the previous mobile generation networks. 5G brings three different services for end user like Extreme mobile broadband (eMBB). It offers high-speed internet connectivity, greater bandwidth, moderate latency, UltraHD streaming videos, virtual reality and augmented reality (AR/VR) media, and many more. Massive machine type communication (eMTC), it provides long-range and broadband machine-type communication at a very cost-effective price with less power consumption. eMTC brings a high data rate service, low power, extended coverage via less device complexity through mobile carriers for IoT applications. Ultra-reliable low latency communication (URLLC) offers low-latency and ultra-high reliability, rich quality of service (QoS), which is not possible with traditional mobile network architecture. URLLC is designed for on-demand real-time interaction such as remote surgery, vehicle to vehicle (V2V) communication, industry 4.0, smart grids, intelligent transport system, etc. 5G faster than 4G and offers remote-controlled operation over a reliable network with zero delays. It provides down-link maximum throughput of up to 20 Gbps. In addition, 5G also supports 4G WWWW (4th Generation World Wide Wireless Web) [ 5 ] and is based on Internet protocol version 6 (IPv6) protocol. 5G provides unlimited internet connection at your convenience, anytime, anywhere with extremely high speed, high throughput, low-latency, higher reliability and scalability, and energy-efficient mobile communication technology [ 6 ]. 5G mainly divided in two parts 6 GHz 5G and Millimeter wave(mmWave) 5G.

6 GHz is a mid frequency band which works as a mid point between capacity and coverage to offer perfect environment for 5G connectivity. 6 GHz spectrum will provide high bandwidth with improved network performance. It offers continuous channels that will reduce the need for network densification when mid-band spectrum is not available and it makes 5G connectivity affordable at anytime, anywhere for everyone.

mmWave is an essential technology of 5G network which build high performance network. 5G mmWave offer diverse services that is why all network providers should add on this technology in their 5G deployment planning. There are lots of service providers who deployed 5G mmWave, and their simulation result shows that 5G mmwave is a far less used spectrum. It provides very high speed wireless communication and it also offers ultra-wide bandwidth for next generation mobile network.

The evolution of wireless mobile technologies are presented in Table 1 . The abbreviations used in this paper are mentioned in Table 2 .

Summary of Mobile Technology.

Table of Notations and Abbreviations.

1.2. Key Contributions

The objective of this survey is to provide a detailed guide of 5G key technologies, methods to researchers, and to help with understanding how the recent works addressed 5G problems and developed solutions to tackle the 5G challenges; i.e., what are new methods that must be applied and how can they solve problems? Highlights of the research article are as follows.

• This survey focused on the recent trends and development in the era of 5G and novel contributions by the researcher community and discussed technical details on essential aspects of the 5G advancement.
• In this paper, the evolution of the mobile network from 1G to 5G is presented. In addition, the growth of mobile communication under different attributes is also discussed.
• This paper covers the emerging applications and research groups working on 5G & different research areas in 5G wireless communication network with a descriptive taxonomy.
• This survey discusses the current vision of the 5G networks, advantages, applications, key technologies, and key features. Furthermore, machine learning prospects are also explored with the emerging requirements in the 5G era. The article also focused on technical aspects of 5G IoT Based approaches and optimization techniques for 5G.
• we provide an extensive overview and recent advancement of emerging technologies of 5G mobile network, namely, MIMO, Non-Orthogonal Multiple Access (NOMA), mmWave, Internet of Things (IoT), Machine Learning (ML), and optimization. Also, a technical summary is discussed by highlighting the context of current approaches and corresponding challenges.
• Security challenges and considerations while developing 5G technology are discussed.
• Finally, the paper concludes with the future directives.

The existing survey focused on architecture, key concepts, and implementation challenges and issues. In contrast, this survey covers the state-of-the-art techniques as well as corresponding recent novel developments by researchers. Various recent significant papers are discussed with the key technologies accelerating the development and production of 5G products.

2. Existing Surveys and Their Applicability

In this paper, a detailed survey on various technologies of 5G networks is presented. Various researchers have worked on different technologies of 5G networks. In this section, Table 3 gives a tabular representation of existing surveys of 5G networks. Massive MIMO, NOMA, small cell, mmWave, beamforming, and MEC are the six main pillars that helped to implement 5G networks in real life.

A comparative overview of existing surveys on different technologies of 5G networks.

2.1. Limitations of Existing Surveys

The existing survey focused on architecture, key concepts, and implementation challenges and issues. The numerous current surveys focused on various 5G technologies with different parameters, and the authors did not cover all the technologies of the 5G network in detail with challenges and recent advancements. Few authors worked on MIMO (Non-Orthogonal Multiple Access) NOMA, MEC, small cell technologies. In contrast, some others worked on beamforming, Millimeter-wave (mmWave). But the existing survey did not cover all the technologies of the 5G network from a research and advancement perspective. No detailed survey is available in the market covering all the 5G network technologies and currently published research trade-offs. So, our main aim is to give a detailed study of all the technologies working on the 5G network. In contrast, this survey covers the state-of-the-art techniques as well as corresponding recent novel developments by researchers. Various recent significant papers are discussed with the key technologies accelerating the development and production of 5G products. This survey article collected key information about 5G technology and recent advancements, and it can be a kind of a guide for the reader. This survey provides an umbrella approach to bring multiple solutions and recent improvements in a single place to accelerate the 5G research with the latest key enabling solutions and reviews. A systematic layout representation of the survey in Figure 1 . We provide a state-of-the-art comparative overview of the existing surveys on different technologies of 5G networks in Table 3 .

Systematic layout representation of survey.

2.2. Article Organization

This article is organized under the following sections. Section 2 presents existing surveys and their applicability. In Section 3 , the preliminaries of 5G technology are presented. In Section 4 , recent advances of 5G technology based on Massive MIMO, NOMA, Millimeter Wave, 5G with IoT, machine learning for 5G, and Optimization in 5G are provided. In Section 5 , a description of novel 5G features over 4G is provided. Section 6 covered all the security concerns of the 5G network. Section 7 , 5G technology based on above-stated challenges summarize in tabular form. Finally, Section 8 and Section 9 conclude the study, which paves the path for future research.

3. Preliminary Section

3.1. emerging 5g paradigms and its features.

5G provides very high speed, low latency, and highly salable connectivity between multiple devices and IoT worldwide. 5G will provide a very flexible model to develop a modern generation of applications and industry goals [ 26 , 27 ]. There are many services offered by 5G network architecture are stated below:

Massive machine to machine communications: 5G offers novel, massive machine-to-machine communications [ 28 ], also known as the IoT [ 29 ], that provide connectivity between lots of machines without any involvement of humans. This service enhances the applications of 5G and provides connectivity between agriculture, construction, and industries [ 30 ].

Ultra-reliable low latency communications (URLLC): This service offers real-time management of machines, high-speed vehicle-to-vehicle connectivity, industrial connectivity and security principles, and highly secure transport system, and multiple autonomous actions. Low latency communications also clear up a different area where remote medical care, procedures, and operation are all achievable [ 31 ].

Enhanced mobile broadband: Enhance mobile broadband is an important use case of 5G system, which uses massive MIMO antenna, mmWave, beamforming techniques to offer very high-speed connectivity across a wide range of areas [ 32 ].

For communities: 5G provides a very flexible internet connection between lots of machines to make smart homes, smart schools, smart laboratories, safer and smart automobiles, and good health care centers [ 33 ].

For businesses and industry: As 5G works on higher spectrum ranges from 24 to 100 GHz. This higher frequency range provides secure low latency communication and high-speed wireless connectivity between IoT devices and industry 4.0, which opens a market for end-users to enhance their business models [ 34 ].

New and Emerging technologies: As 5G came up with many new technologies like beamforming, massive MIMO, mmWave, small cell, NOMA, MEC, and network slicing, it introduced many new features to the market. Like virtual reality (VR), users can experience the physical presence of people who are millions of kilometers away from them. Many new technologies like smart homes, smart workplaces, smart schools, smart sports academy also came into the market with this 5G Mobile network model [ 35 ].

3.2. Commercial Service Providers of 5G

5G provides high-speed internet browsing, streaming, and downloading with very high reliability and low latency. 5G network will change your working style, and it will increase new business opportunities and provide innovations that we cannot imagine. This section covers top service providers of 5G network [ 36 , 37 ].

Ericsson: Ericsson is a Swedish multinational networking and telecommunications company, investing around 25.62 billion USD in 5G network, which makes it the biggest telecommunication company. It claims that it is the only company working on all the continents to make the 5G network a global standard for the next generation wireless communication. Ericsson developed the first 5G radio prototype that enables the operators to set up the live field trials in their network, which helps operators understand how 5G reacts. It plays a vital role in the development of 5G hardware. It currently provides 5G services in over 27 countries with content providers like China Mobile, GCI, LGU+, AT&T, Rogers, and many more. It has 100 commercial agreements with different operators as of 2020.

Verizon: It is American multinational telecommunication which was founded in 1983. Verizon started offering 5G services in April 2020, and by December 2020, it has actively provided 5G services in 30 cities of the USA. They planned that by the end of 2021, they would deploy 5G in 30 more new cities. Verizon deployed a 5G network on mmWave, a very high band spectrum between 30 to 300 GHz. As it is a significantly less used spectrum, it provides very high-speed wireless communication. MmWave offers ultra-wide bandwidth for next-generation mobile networks. MmWave is a faster and high-band spectrum that has a limited range. Verizon planned to increase its number of 5G cells by 500% by 2020. Verizon also has an ultra wide-band flagship 5G service which is the best 5G service that increases the market price of Verizon.

Nokia: Nokia is a Finnish multinational telecommunications company which was founded in 1865. Nokia is one of the companies which adopted 5G technology very early. It is developing, researching, and building partnerships with various 5G renders to offer 5G communication as soon as possible. Nokia collaborated with Deutsche Telekom and Hamburg Port Authority and provided them 8000-hectare site for their 5G MoNArch project. Nokia is the only company that supplies 5G technology to all the operators of different countries like AT&T, Sprint, T-Mobile US and Verizon in the USA, Korea Telecom, LG U+ and SK Telecom in South Korea and NTT DOCOMO, KDDI, and SoftBank in Japan. Presently, Nokia has around 150+ agreements and 29 live networks all over the world. Nokia is continuously working hard on 5G technology to expand 5G networks all over the globe.

AT&T: AT&T is an American multinational company that was the first to deploy a 5G network in reality in 2018. They built a gigabit 5G network connection in Waco, TX, Kalamazoo, MI, and South Bend to achieve this. It is the first company that archives 1–2 gigabit per second speed in 2019. AT&T claims that it provides a 5G network connection among 225 million people worldwide by using a 6 GHz spectrum band.

T-Mobile: T-Mobile US (TMUS) is an American wireless network operator which was the first service provider that offers a real 5G nationwide network. The company knew that high-band 5G was not feasible nationwide, so they used a 600 MHz spectrum to build a significant portion of its 5G network. TMUS is planning that by 2024 they will double the total capacity and triple the full 5G capacity of T-Mobile and Sprint combined. The sprint buyout is helping T-Mobile move forward the company’s current market price to 129.98 USD.

Samsung: Samsung started their research in 5G technology in 2011. In 2013, Samsung successfully developed the world’s first adaptive array transceiver technology operating in the millimeter-wave Ka bands for cellular communications. Samsung provides several hundred times faster data transmission than standard 4G for core 5G mobile communication systems. The company achieved a lot of success in the next generation of technology, and it is considered one of the leading companies in the 5G domain.

Qualcomm: Qualcomm is an American multinational corporation in San Diego, California. It is also one of the leading company which is working on 5G chip. Qualcomm’s first 5G modem chip was announced in October 2016, and a prototype was demonstrated in October 2017. Qualcomm mainly focuses on building products while other companies talk about 5G; Qualcomm is building the technologies. According to one magazine, Qualcomm was working on three main areas of 5G networks. Firstly, radios that would use bandwidth from any network it has access to; secondly, creating more extensive ranges of spectrum by combining smaller pieces; and thirdly, a set of services for internet applications.

ZTE Corporation: ZTE Corporation was founded in 1985. It is a partially Chinese state-owned technology company that works in telecommunication. It was a leading company that worked on 4G LTE, and it is still maintaining its value and doing research and tests on 5G. It is the first company that proposed Pre5G technology with some series of solutions.

NEC Corporation: NEC Corporation is a Japanese multinational information technology and electronics corporation headquartered in Minato, Tokyo. ZTE also started their research on 5G, and they introduced a new business concept. NEC’s main aim is to develop 5G NR for the global mobile system and create secure and intelligent technologies to realize 5G services.

Cisco: Cisco is a USA networking hardware company that also sleeves up for 5G network. Cisco’s primary focus is to support 5G in three ways: Service—enable 5G services faster so all service providers can increase their business. Infrastructure—build 5G-oriented infrastructure to implement 5G more quickly. Automation—make a more scalable, flexible, and reliable 5G network. The companies know the importance of 5G, and they want to connect more than 30 billion devices in the next couple of years. Cisco intends to work on network hardening as it is a vital part of 5G network. Cisco used AI with deep learning to develop a 5G Security Architecture, enabling Secure Network Transformation.

3.3. 5G Research Groups

Many research groups from all over the world are working on a 5G wireless mobile network [ 38 ]. These groups are continuously working on various aspects of 5G. The list of those research groups are presented as follows: 5GNOW (5th Generation Non-Orthogonal Waveform for Asynchronous Signaling), NEWCOM (Network of Excellence in Wireless Communication), 5GIC (5G Innovation Center), NYU (New York University) Wireless, 5GPPP (5G Infrastructure Public-Private Partnership), EMPHATIC (Enhanced Multi-carrier Technology for Professional Adhoc and Cell-Based Communication), ETRI(Electronics and Telecommunication Research Institute), METIS (Mobile and wireless communication Enablers for the Twenty-twenty Information Society) [ 39 ]. The various research groups along with the research area are presented in Table 4 .

Research groups working on 5G mobile networks.

3.4. 5G Applications

5G is faster than 4G and offers remote-controlled operation over a reliable network with zero delays. It provides down-link maximum throughput of up to 20 Gbps. In addition, 5G also supports 4G WWWW (4th Generation World Wide Wireless Web) [ 5 ] and is based on Internet protocol version 6 (IPv6) protocol. 5G provides unlimited internet connection at your convenience, anytime, anywhere with extremely high speed, high throughput, low-latency, higher reliability, greater scalablility, and energy-efficient mobile communication technology [ 6 ].

There are lots of applications of 5G mobile network are as follows:

• High-speed mobile network: 5G is an advancement on all the previous mobile network technologies, which offers very high speed downloading speeds 0 of up to 10 to 20 Gbps. The 5G wireless network works as a fiber optic internet connection. 5G is different from all the conventional mobile transmission technologies, and it offers both voice and high-speed data connectivity efficiently. 5G offers very low latency communication of less than a millisecond, useful for autonomous driving and mission-critical applications. 5G will use millimeter waves for data transmission, providing higher bandwidth and a massive data rate than lower LTE bands. As 5 Gis a fast mobile network technology, it will enable virtual access to high processing power and secure and safe access to cloud services and enterprise applications. Small cell is one of the best features of 5G, which brings lots of advantages like high coverage, high-speed data transfer, power saving, easy and fast cloud access, etc. [ 40 ].
• Entertainment and multimedia: In one analysis in 2015, it was found that more than 50 percent of mobile internet traffic was used for video downloading. This trend will surely increase in the future, which will make video streaming more common. 5G will offer High-speed streaming of 4K videos with crystal clear audio, and it will make a high definition virtual world on your mobile. 5G will benefit the entertainment industry as it offers 120 frames per second with high resolution and higher dynamic range video streaming, and HD TV channels can also be accessed on mobile devices without any interruptions. 5G provides low latency high definition communication so augmented reality (AR), and virtual reality (VR) will be very easily implemented in the future. Virtual reality games are trendy these days, and many companies are investing in HD virtual reality games. The 5G network will offer high-speed internet connectivity with a better gaming experience [ 41 ].
• Smart homes : smart home appliances and products are in demand these days. The 5G network makes smart homes more real as it offers high-speed connectivity and monitoring of smart appliances. Smart home appliances are easily accessed and configured from remote locations using the 5G network as it offers very high-speed low latency communication.
• Smart cities: 5G wireless network also helps develop smart cities applications such as automatic traffic management, weather update, local area broadcasting, energy-saving, efficient power supply, smart lighting system, water resource management, crowd management, emergency control, etc.
• Industrial IoT: 5G wireless technology will provide lots of features for future industries such as safety, process tracking, smart packing, shipping, energy efficiency, automation of equipment, predictive maintenance, and logistics. 5G smart sensor technology also offers smarter, safer, cost-effective, and energy-saving industrial IoT operations.
• Smart Farming: 5G technology will play a crucial role in agriculture and smart farming. 5G sensors and GPS technology will help farmers track live attacks on crops and manage them quickly. These smart sensors can also be used for irrigation, pest, insect, and electricity control.
• Autonomous Driving: The 5G wireless network offers very low latency high-speed communication, significant for autonomous driving. It means self-driving cars will come to real life soon with 5G wireless networks. Using 5G autonomous cars can easily communicate with smart traffic signs, objects, and other vehicles running on the road. 5G’s low latency feature makes self-driving more real as every millisecond is essential for autonomous vehicles, decision-making is done in microseconds to avoid accidents.
• Healthcare and mission-critical applications: 5G technology will bring modernization in medicine where doctors and practitioners can perform advanced medical procedures. The 5G network will provide connectivity between all classrooms, so attending seminars and lectures will be easier. Through 5G technology, patients can connect with doctors and take their advice. Scientists are building smart medical devices which can help people with chronic medical conditions. The 5G network will boost the healthcare industry with smart devices, the internet of medical things, smart sensors, HD medical imaging technologies, and smart analytics systems. 5G will help access cloud storage, so accessing healthcare data will be very easy from any location worldwide. Doctors and medical practitioners can easily store and share large files like MRI reports within seconds using the 5G network.
• Satellite Internet: In many remote areas, ground base stations are not available, so 5G will play a crucial role in providing connectivity in such areas. The 5G network will provide connectivity using satellite systems, and the satellite system uses a constellation of multiple small satellites to provide connectivity in urban and rural areas across the world.

4. 5G Technologies

This section describes recent advances of 5G Massive MIMO, 5G NOMA, 5G millimeter wave, 5G IOT, 5G with machine learning, and 5G optimization-based approaches. In addition, the summary is also presented in each subsection that paves the researchers for the future research direction.

4.1. 5G Massive MIMO

Multiple-input-multiple-out (MIMO) is a very important technology for wireless systems. It is used for sending and receiving multiple signals simultaneously over the same radio channel. MIMO plays a very big role in WI-FI, 3G, 4G, and 4G LTE-A networks. MIMO is mainly used to achieve high spectral efficiency and energy efficiency but it was not up to the mark MIMO provides low throughput and very low reliable connectivity. To resolve this, lots of MIMO technology like single user MIMO (SU-MIMO), multiuser MIMO (MU-MIMO) and network MIMO were used. However, these new MIMO also did not still fulfill the demand of end users. Massive MIMO is an advancement of MIMO technology used in the 5G network in which hundreds and thousands of antennas are attached with base stations to increase throughput and spectral efficiency. Multiple transmit and receive antennas are used in massive MIMO to increase the transmission rate and spectral efficiency. When multiple UEs generate downlink traffic simultaneously, massive MIMO gains higher capacity. Massive MIMO uses extra antennas to move energy into smaller regions of space to increase spectral efficiency and throughput [ 43 ]. In traditional systems data collection from smart sensors is a complex task as it increases latency, reduced data rate and reduced reliability. While massive MIMO with beamforming and huge multiplexing techniques can sense data from different sensors with low latency, high data rate and higher reliability. Massive MIMO will help in transmitting the data in real-time collected from different sensors to central monitoring locations for smart sensor applications like self-driving cars, healthcare centers, smart grids, smart cities, smart highways, smart homes, and smart enterprises [ 44 ].

Highlights of 5G Massive MIMO technology are as follows:

• Data rate: Massive MIMO is advised as the one of the dominant technologies to provide wireless high speed and high data rate in the gigabits per seconds.
• The relationship between wave frequency and antenna size: Both are inversely proportional to each other. It means lower frequency signals need a bigger antenna and vise versa.

Pictorial representation of multi-input and multi-output (MIMO).

• MIMO role in 5G: Massive MIMO will play a crucial role in the deployment of future 5G mobile communication as greater spectral and energy efficiency could be enabled.

State-of-the-Art Approaches

Plenty of approaches were proposed to resolve the issues of conventional MIMO [ 7 ].

The MIMO multirate, feed-forward controller is suggested by Mae et al. [ 46 ]. In the simulation, the proposed model generates the smooth control input, unlike the conventional MIMO, which generates oscillated control inputs. It also outperformed concerning the error rate. However, a combination of multirate and single rate can be used for better results.

The performance of stand-alone MIMO, distributed MIMO with and without corporation MIMO, was investigated by Panzner et al. [ 47 ]. In addition, an idea about the integration of large scale in the 5G technology was also presented. In the experimental analysis, different MIMO configurations are considered. The variation in the ratio of overall transmit antennas to spatial is deemed step-wise from equality to ten.

The simulation of massive MIMO noncooperative and cooperative systems for down-link behavior was performed by He et al. [ 48 ]. It depends on present LTE systems, which deal with various antennas in the base station set-up. It was observed that collaboration in different BS improves the system behaviors, whereas throughput is reduced slightly in this approach. However, a new method can be developed which can enhance both system behavior and throughput.

In [ 8 ], different approaches that increased the energy efficiency benefits provided by massive MIMO were presented. They analyzed the massive MIMO technology and described the detailed design of the energy consumption model for massive MIMO systems. This article has explored several techniques to enhance massive MIMO systems’ energy efficiency (EE) gains. This paper reviews standard EE-maximization approaches for the conventional massive MIMO systems, namely, scaling number of antennas, real-time implementing low-complexity operations at the base station (BS), power amplifier losses minimization, and radio frequency (RF) chain minimization requirements. In addition, open research direction is also identified.

In [ 49 ], various existing approaches based on different antenna selection and scheduling, user selection and scheduling, and joint antenna and user scheduling methods adopted in massive MIMO systems are presented in this paper. The objective of this survey article was to make awareness about the current research and future research direction in MIMO for systems. They analyzed that complete utilization of resources and bandwidth was the most crucial factor which enhances the sum rate.

In [ 50 ], authors discussed the development of various techniques for pilot contamination. To calculate the impact of pilot contamination in time division duplex (TDD) massive MIMO system, TDD and frequency division duplexing FDD patterns in massive MIMO techniques are used. They discussed different issues in pilot contamination in TDD massive MIMO systems with all the possible future directions of research. They also classified various techniques to generate the channel information for both pilot-based and subspace-based approaches.

In [ 19 ], the authors defined the uplink and downlink services for a massive MIMO system. In addition, it maintains a performance matrix that measures the impact of pilot contamination on different performances. They also examined the various application of massive MIMO such as small cells, orthogonal frequency-division multiplexing (OFDM) schemes, massive MIMO IEEE 802, 3rd generation partnership project (3GPP) specifications, and higher frequency bands. They considered their research work crucial for cutting edge massive MIMO and covered many issues like system throughput performance and channel state acquisition at higher frequencies.

In [ 13 ], various approaches were suggested for MIMO future generation wireless communication. They made a comparative study based on performance indicators such as peak data rate, energy efficiency, latency, throughput, etc. The key findings of this survey are as follows: (1) spatial multiplexing improves the energy efficiency; (2) design of MIMO play a vital role in the enhancement of throughput; (3) enhancement of mMIMO focusing on energy & spectral performance; (4) discussed the future challenges to improve the system design.

In [ 51 ], the study of large-scale MIMO systems for an energy-efficient system sharing method was presented. For the resource allocation, circuit energy and transmit energy expenditures were taken into consideration. In addition, the optimization techniques were applied for an energy-efficient resource sharing system to enlarge the energy efficiency for individual QoS and energy constraints. The author also examined the BS configuration, which includes homogeneous and heterogeneous UEs. While simulating, they discussed that the total number of transmit antennas plays a vital role in boosting energy efficiency. They highlighted that the highest energy efficiency was obtained when the BS was set up with 100 antennas that serve 20 UEs.

This section includes various works done on 5G MIMO technology by different author’s. Table 5 shows how different author’s worked on improvement of various parameters such as throughput, latency, energy efficiency, and spectral efficiency with 5G MIMO technology.

Summary of massive MIMO-based approaches in 5G technology.

4.2. 5G Non-Orthogonal Multiple Access (NOMA)

NOMA is a very important radio access technology used in next generation wireless communication. Compared to previous orthogonal multiple access techniques, NOMA offers lots of benefits like high spectrum efficiency, low latency with high reliability and high speed massive connectivity. NOMA mainly works on a baseline to serve multiple users with the same resources in terms of time, space and frequency. NOMA is mainly divided into two main categories one is code domain NOMA and another is power domain NOMA. Code-domain NOMA can improve the spectral efficiency of mMIMO, which improves the connectivity in 5G wireless communication. Code-domain NOMA was divided into some more multiple access techniques like sparse code multiple access, lattice-partition multiple access, multi-user shared access and pattern-division multiple access [ 52 ]. Power-domain NOMA is widely used in 5G wireless networks as it performs well with various wireless communication techniques such as MIMO, beamforming, space-time coding, network coding, full-duplex and cooperative communication etc. [ 53 ]. The conventional orthogonal frequency-division multiple access (OFDMA) used by 3GPP in 4G LTE network provides very low spectral efficiency when bandwidth resources are allocated to users with low channel state information (CSI). NOMA resolved this issue as it enables users to access all the subcarrier channels so bandwidth resources allocated to the users with low CSI can still be accessed by the users with strong CSI which increases the spectral efficiency. The 5G network will support heterogeneous architecture in which small cell and macro base stations work for spectrum sharing. NOMA is a key technology of the 5G wireless system which is very helpful for heterogeneous networks as multiple users can share their data in a small cell using the NOMA principle.The NOMA is helpful in various applications like ultra-dense networks (UDN), machine to machine (M2M) communication and massive machine type communication (mMTC). As NOMA provides lots of features it has some challenges too such as NOMA needs huge computational power for a large number of users at high data rates to run the SIC algorithms. Second, when users are moving from the networks, to manage power allocation optimization is a challenging task for NOMA [ 54 ]. Hybrid NOMA (HNOMA) is a combination of power-domain and code-domain NOMA. HNOMA uses both power differences and orthogonal resources for transmission among multiple users. As HNOMA is using both power-domain NOMA and code-domain NOMA it can achieve higher spectral efficiency than Power-domain NOMA and code-domain NOMA. In HNOMA multiple groups can simultaneously transmit signals at the same time. It uses a message passing algorithm (MPA) and successive interference cancellation (SIC)-based detection at the base station for these groups [ 55 ].

Highlights of 5G NOMA technology as follows:

Pictorial representation of orthogonal and Non-Orthogonal Multiple Access (NOMA).

• NOMA provides higher data rates and resolves all the loop holes of OMA that makes 5G mobile network more scalable and reliable.
• As multiple users use same frequency band simultaneously it increases the performance of whole network.
• To setup intracell and intercell interference NOMA provides nonorthogonal transmission on the transmitter end.
• The primary fundamental of NOMA is to improve the spectrum efficiency by strengthening the ramification of receiver.

State-of-the-Art of Approaches

A plenty of approaches were developed to address the various issues in NOMA.

A novel approach to address the multiple receiving signals at the same frequency is proposed in [ 22 ]. In NOMA, multiple users use the same sub-carrier, which improves the fairness and throughput of the system. As a nonorthogonal method is used among multiple users, at the time of retrieving the user’s signal at the receiver’s end, joint processing is required. They proposed solutions to optimize the receiver and the radio resource allocation of uplink NOMA. Firstly, the authors proposed an iterative MUDD which utilizes the information produced by the channel decoder to improve the performance of the multiuser detector. After that, the author suggested a power allocation and novel subcarrier that enhances the users’ weighted sum rate for the NOMA scheme. Their proposed model showed that NOMA performed well as compared to OFDM in terms of fairness and efficiency.

In [ 53 ], the author’s reviewed a power-domain NOMA that uses superposition coding (SC) and successive interference cancellation (SIC) at the transmitter and the receiver end. Lots of analyses were held that described that NOMA effectively satisfies user data rate demands and network-level of 5G technologies. The paper presented a complete review of recent advances in the 5G NOMA system. It showed the comparative analysis regarding allocation procedures, user fairness, state-of-the-art efficiency evaluation, user pairing pattern, etc. The study also analyzes NOMA’s behavior when working with other wireless communication techniques, namely, beamforming, MIMO, cooperative connections, network, space-time coding, etc.

In [ 9 ], the authors proposed NOMA with MEC, which improves the QoS as well as reduces the latency of the 5G wireless network. This model increases the uplink NOMA by decreasing the user’s uplink energy consumption. They formulated an optimized NOMA framework that reduces the energy consumption of MEC by using computing and communication resource allocation, user clustering, and transmit powers.

In [ 10 ], the authors proposed a model which investigates outage probability under average channel state information CSI and data rate in full CSI to resolve the problem of optimal power allocation, which increase the NOMA downlink system among users. They developed simple low-complexity algorithms to provide the optimal solution. The obtained simulation results showed NOMA’s efficiency, achieving higher performance fairness compared to the TDMA configurations. It was observed from the results that NOMA, through the appropriate power amplifiers (PA), ensures the high-performance fairness requirement for the future 5G wireless communication networks.

In [ 56 ], researchers discussed that the NOMA technology and waveform modulation techniques had been used in the 5G mobile network. Therefore, this research gave a detailed survey of non-orthogonal waveform modulation techniques and NOMA schemes for next-generation mobile networks. By analyzing and comparing multiple access technologies, they considered the future evolution of these technologies for 5G mobile communication.

In [ 57 ], the authors surveyed non-orthogonal multiple access (NOMA) from the development phase to the recent developments. They have also compared NOMA techniques with traditional OMA techniques concerning information theory. The author discussed the NOMA schemes categorically as power and code domain, including the design principles, operating principles, and features. Comparison is based upon the system’s performance, spectral efficiency, and the receiver’s complexity. Also discussed are the future challenges, open issues, and their expectations of NOMA and how it will support the key requirements of 5G mobile communication systems with massive connectivity and low latency.

In [ 17 ], authors present the first review of an elementary NOMA model with two users, which clarify its central precepts. After that, a general design with multicarrier supports with a random number of users on each sub-carrier is analyzed. In performance evaluation with the existing approaches, resource sharing and multiple-input multiple-output NOMA are examined. Furthermore, they took the key elements of NOMA and its potential research demands. Finally, they reviewed the two-user SC-NOMA design and a multi-user MC-NOMA design to highlight NOMA’s basic approaches and conventions. They also present the research study about the performance examination, resource assignment, and MIMO in NOMA.

In this section, various works by different authors done on 5G NOMA technology is covered. Table 6 shows how other authors worked on the improvement of various parameters such as spectral efficiency, fairness, and computing capacity with 5G NOMA technology.

Summary of NOMA-based approaches in 5G technology.

4.3. 5G Millimeter Wave (mmWave)

Millimeter wave is an extremely high frequency band, which is very useful for 5G wireless networks. MmWave uses 30 GHz to 300 GHz spectrum band for transmission. The frequency band between 30 GHz to 300 GHz is known as mmWave because these waves have wavelengths between 1 to 10 mm. Till now radar systems and satellites are only using mmWave as these are very fast frequency bands which provide very high speed wireless communication. Many mobile network providers also started mmWave for transmitting data between base stations. Using two ways the speed of data transmission can be improved one is by increasing spectrum utilization and second is by increasing spectrum bandwidth. Out of these two approaches increasing bandwidth is quite easy and better. The frequency band below 5 GHz is very crowded as many technologies are using it so to boost up the data transmission rate 5G wireless network uses mmWave technology which instead of increasing spectrum utilization, increases the spectrum bandwidth [ 58 ]. To maximize the signal bandwidth in wireless communication the carrier frequency should also be increased by 5% because the signal bandwidth is directly proportional to carrier frequencies. The frequency band between 28 GHz to 60 GHz is very useful for 5G wireless communication as 28 GHz frequency band offers up to 1 GHz spectrum bandwidth and 60 GHz frequency band offers 2 GHz spectrum bandwidth. 4G LTE provides 2 GHz carrier frequency which offers only 100 MHz spectrum bandwidth. However, the use of mmWave increases the spectrum bandwidth 10 times, which leads to better transmission speeds [ 59 , 60 ].

Highlights of 5G mmWave are as follows:

Pictorial representation of millimeter wave.

• The 5G mmWave offer three advantages: (1) MmWave is very less used new Band, (2) MmWave signals carry more data than lower frequency wave, and (3) MmWave can be incorporated with MIMO antenna with the potential to offer a higher magnitude capacity compared to current communication systems.

In [ 11 ], the authors presented the survey of mmWave communications for 5G. The advantage of mmWave communications is adaptability, i.e., it supports the architectures and protocols up-gradation, which consists of integrated circuits, systems, etc. The authors over-viewed the present solutions and examined them concerning effectiveness, performance, and complexity. They also discussed the open research issues of mmWave communications in 5G concerning the software-defined network (SDN) architecture, network state information, efficient regulation techniques, and the heterogeneous system.

In [ 61 ], the authors present the recent work done by investigators in 5G; they discussed the design issues and demands of mmWave 5G antennas for cellular handsets. After that, they designed a small size and low-profile 60 GHz array of antenna units that contain 3D planer mesh-grid antenna elements. For the future prospect, a framework is designed in which antenna components are used to operate cellular handsets on mmWave 5G smartphones. In addition, they cross-checked the mesh-grid array of antennas with the polarized beam for upcoming hardware challenges.

In [ 12 ], the authors considered the suitability of the mmWave band for 5G cellular systems. They suggested a resource allocation system for concurrent D2D communications in mmWave 5G cellular systems, and it improves network efficiency and maintains network connectivity. This research article can serve as guidance for simulating D2D communications in mmWave 5G cellular systems. Massive mmWave BS may be set up to obtain a high delivery rate and aggregate efficiency. Therefore, many wireless users can hand off frequently between the mmWave base terminals, and it emerges the demand to search the neighbor having better network connectivity.

In [ 62 ], the authors provided a brief description of the cellular spectrum which ranges from 1 GHz to 3 GHz and is very crowed. In addition, they presented various noteworthy factors to set up mmWave communications in 5G, namely, channel characteristics regarding mmWave signal attenuation due to free space propagation, atmospheric gaseous, and rain. In addition, hybrid beamforming architecture in the mmWave technique is analyzed. They also suggested methods for the blockage effect in mmWave communications due to penetration damage. Finally, the authors have studied designing the mmWave transmission with small beams in nonorthogonal device-to-device communication.

This section covered various works done on 5G mmWave technology. The Table 7 shows how different author’s worked on the improvement of various parameters i.e., transmission rate, coverage, and cost, with 5G mmWave technology.

Summary of existing mmWave-based approaches in 5G technology.

4.4. 5G IoT Based Approaches

The 5G mobile network plays a big role in developing the Internet of Things (IoT). IoT will connect lots of things with the internet like appliances, sensors, devices, objects, and applications. These applications will collect lots of data from different devices and sensors. 5G will provide very high speed internet connectivity for data collection, transmission, control, and processing. 5G is a flexible network with unused spectrum availability and it offers very low cost deployment that is why it is the most efficient technology for IoT [ 63 ]. In many areas, 5G provides benefits to IoT, and below are some examples:

Smart homes: smart home appliances and products are in demand these days. The 5G network makes smart homes more real as it offers high speed connectivity and monitoring of smart appliances. Smart home appliances are easily accessed and configured from remote locations using the 5G network, as it offers very high speed low latency communication.

Smart cities: 5G wireless network also helps in developing smart cities applications such as automatic traffic management, weather update, local area broadcasting, energy saving, efficient power supply, smart lighting system, water resource management, crowd management, emergency control, etc.

Industrial IoT: 5G wireless technology will provide lots of features for future industries such as safety, process tracking, smart packing, shipping, energy efficiency, automation of equipment, predictive maintenance and logistics. 5G smart sensor technology also offers smarter, safer, cost effective, and energy-saving industrial operation for industrial IoT.

Smart Farming: 5G technology will play a crucial role for agriculture and smart farming. 5G sensors and GPS technology will help farmers to track live attacks on crops and manage them quickly. These smart sensors can also be used for irrigation control, pest control, insect control, and electricity control.

Autonomous Driving: 5G wireless network offers very low latency high speed communication which is very significant for autonomous driving. It means self-driving cars will come to real life soon with 5G wireless networks. Using 5G autonomous cars can easily communicate with smart traffic signs, objects and other vehicles running on the road. 5G’s low latency feature makes self-driving more real as every millisecond is important for autonomous vehicles, decision taking is performed in microseconds to avoid accidents [ 64 ].

Highlights of 5G IoT are as follows:

Pictorial representation of IoT with 5G.

• 5G with IoT is a new feature of next-generation mobile communication, which provides a high-speed internet connection between moderated devices. 5G IoT also offers smart homes, smart devices, sensors, smart transportation systems, smart industries, etc., for end-users to make them smarter.
• IoT deals with moderate devices which connect through the internet. The approach of the IoT has made the consideration of the research associated with the outcome of providing wearable, smart-phones, sensors, smart transportation systems, smart devices, washing machines, tablets, etc., and these diverse systems are associated to a common interface with the intelligence to connect.
• Significant IoT applications include private healthcare systems, traffic management, industrial management, and tactile internet, etc.

Plenty of approaches is devised to address the issues of IoT [ 14 , 65 , 66 ].

In [ 65 ], the paper focuses on 5G mobile systems due to the emerging trends and developing technologies, which results in the exponential traffic growth in IoT. The author surveyed the challenges and demands during deployment of the massive IoT applications with the main focus on mobile networking. The author reviewed the features of standard IoT infrastructure, along with the cellular-based, low-power wide-area technologies (LPWA) such as eMTC, extended coverage (EC)-GSM-IoT, as well as noncellular, low-power wide-area (LPWA) technologies such as SigFox, LoRa etc.

In [ 14 ], the authors presented how 5G technology copes with the various issues of IoT today. It provides a brief review of existing and forming 5G architectures. The survey indicates the role of 5G in the foundation of the IoT ecosystem. IoT and 5G can easily combine with improved wireless technologies to set up the same ecosystem that can fulfill the current requirement for IoT devices. 5G can alter nature and will help to expand the development of IoT devices. As the process of 5G unfolds, global associations will find essentials for setting up a cross-industry engagement in determining and enlarging the 5G system.

In [ 66 ], the author introduced an IoT authentication scheme in a 5G network, with more excellent reliability and dynamic. The scheme proposed a privacy-protected procedure for selecting slices; it provided an additional fog node for proper data transmission and service types of the subscribers, along with service-oriented authentication and key understanding to maintain the secrecy, precision of users, and confidentiality of service factors. Users anonymously identify the IoT servers and develop a vital channel for service accessibility and data cached on local fog nodes and remote IoT servers. The author performed a simulation to manifest the security and privacy preservation of the user over the network.

This section covered various works done on 5G IoT by multiple authors. Table 8 shows how different author’s worked on the improvement of numerous parameters, i.e., data rate, security requirement, and performance with 5G IoT.

Summary of IoT-based approaches in 5G technology.

4.5. Machine Learning Techniques for 5G

Various machine learning (ML) techniques were applied in 5G networks and mobile communication. It provides a solution to multiple complex problems, which requires a lot of hand-tuning. ML techniques can be broadly classified as supervised, unsupervised, and reinforcement learning. Let’s discuss each learning technique separately and where it impacts the 5G network.

Supervised Learning, where user works with labeled data; some 5G network problems can be further categorized as classification and regression problems. Some regression problems such as scheduling nodes in 5G and energy availability can be predicted using Linear Regression (LR) algorithm. To accurately predict the bandwidth and frequency allocation Statistical Logistic Regression (SLR) is applied. Some supervised classifiers are applied to predict the network demand and allocate network resources based on the connectivity performance; it signifies the topology setup and bit rates. Support Vector Machine (SVM) and NN-based approximation algorithms are used for channel learning based on observable channel state information. Deep Neural Network (DNN) is also employed to extract solutions for predicting beamforming vectors at the BS’s by taking mapping functions and uplink pilot signals into considerations.

In unsupervised Learning, where the user works with unlabeled data, various clustering techniques are applied to enhance network performance and connectivity without interruptions. K-means clustering reduces the data travel by storing data centers content into clusters. It optimizes the handover estimation based on mobility pattern and selection of relay nodes in the V2V network. Hierarchical clustering reduces network failure by detecting the intrusion in the mobile wireless network; unsupervised soft clustering helps in reducing latency by clustering fog nodes. The nonparametric Bayesian unsupervised learning technique reduces traffic in the network by actively serving the user’s requests and demands. Other unsupervised learning techniques such as Adversarial Auto Encoders (AAE) and Affinity Propagation Clustering techniques detect irregular behavior in the wireless spectrum and manage resources for ultradense small cells, respectively.

In case of an uncertain environment in the 5G wireless network, reinforcement learning (RL) techniques are employed to solve some problems. Actor-critic reinforcement learning is used for user scheduling and resource allocation in the network. Markov decision process (MDP) and Partially Observable MDP (POMDP) is used for Quality of Experience (QoE)-based handover decision-making for Hetnets. Controls packet call admission in HetNets and channel access process for secondary users in a Cognitive Radio Network (CRN). Deep RL is applied to decide the communication channel and mobility and speeds up the secondary user’s learning rate using an antijamming strategy. Deep RL is employed in various 5G network application parameters such as resource allocation and security [ 67 ]. Table 9 shows the state-of-the-art ML-based solution for 5G network.

The state-of-the-art ML-based solution for 5G network.

Highlights of machine learning techniques for 5G are as follows:

Pictorial representation of machine learning (ML) in 5G.

• In ML, a model will be defined which fulfills the desired requirements through which desired results are obtained. In the later stage, it examines accuracy from obtained results.
• ML plays a vital role in 5G network analysis for threat detection, network load prediction, final arrangement, and network formation. Searching for a better balance between power, length of antennas, area, and network thickness crossed with the spontaneous use of services in the universe of individual users and types of devices.

In [ 79 ], author’s firstly describes the demands for the traditional authentication procedures and benefits of intelligent authentication. The intelligent authentication method was established to improve security practice in 5G-and-beyond wireless communication systems. Thereafter, the machine learning paradigms for intelligent authentication were organized into parametric and non-parametric research methods, as well as supervised, unsupervised, and reinforcement learning approaches. As a outcome, machine learning techniques provide a new paradigm into authentication under diverse network conditions and unstable dynamics. In addition, prompt intelligence to the security management to obtain cost-effective, better reliable, model-free, continuous, and situation-aware authentication.

In [ 68 ], the authors proposed a machine learning-based model to predict the traffic load at a particular location. They used a mobile network traffic dataset to train a model that can calculate the total number of user requests at a time. To launch access and mobility management function (AMF) instances according to the requirement as there were no predictions of user request the performance automatically degrade as AMF does not handle these requests at a time. Earlier threshold-based techniques were used to predict the traffic load, but that approach took too much time; therefore, the authors proposed RNN algorithm-based ML to predict the traffic load, which gives efficient results.

In [ 15 ], authors discussed the issue of network slice admission, resource allocation among subscribers, and how to maximize the profit of infrastructure providers. The author proposed a network slice admission control algorithm based on SMDP (decision-making process) that guarantees the subscribers’ best acceptance policies and satisfiability (tenants). They also suggested novel N3AC, a neural network-based algorithm that optimizes performance under various configurations, significantly outperforms practical and straightforward approaches.

This section includes various works done on 5G ML by different authors. Table 10 shows the state-of-the-art work on the improvement of various parameters such as energy efficiency, Quality of Services (QoS), and latency with 5G ML.

The state-of-the-art ML-based approaches in 5G technology.

4.6. Optimization Techniques for 5G

Optimization techniques may be applied to capture NP-Complete or NP-Hard problems in 5G technology. This section briefly describes various research works suggested for 5G technology based on optimization techniques.

In [ 80 ], Massive MIMO technology is used in 5G mobile network to make it more flexible and scalable. The MIMO implementation in 5G needs a significant number of radio frequencies is required in the RF circuit that increases the cost and energy consumption of the 5G network. This paper provides a solution that increases the cost efficiency and energy efficiency with many radio frequency chains for a 5G wireless communication network. They give an optimized energy efficient technique for MIMO antenna and mmWave technologies based 5G mobile communication network. The proposed Energy Efficient Hybrid Precoding (EEHP) algorithm to increase the energy efficiency for the 5G wireless network. This algorithm minimizes the cost of an RF circuit with a large number of RF chains.

In [ 16 ], authors have discussed the growing demand for energy efficiency in the next-generation networks. In the last decade, they have figured out the things in wireless transmissions, which proved a change towards pursuing green communication for the next generation system. The importance of adopting the correct EE metric was also reviewed. Further, they worked through the different approaches that can be applied in the future for increasing the network’s energy and posed a summary of the work that was completed previously to enhance the energy productivity of the network using these capabilities. A system design for EE development using relay selection was also characterized, along with an observation of distinct algorithms applied for EE in relay-based ecosystems.

In [ 81 ], authors presented how AI-based approach is used to the setup of Self Organizing Network (SON) functionalities for radio access network (RAN) design and optimization. They used a machine learning approach to predict the results for 5G SON functionalities. Firstly, the input was taken from various sources; then, prediction and clustering-based machine learning models were applied to produce the results. Multiple AI-based devices were used to extract the knowledge analysis to execute SON functionalities smoothly. Based on results, they tested how self-optimization, self-testing, and self-designing are done for SON. The author also describes how the proposed mechanism classifies in different orders.

In [ 82 ], investigators examined the working of OFDM in various channel environments. They also figured out the changes in frame duration of the 5G TDD frame design. Subcarrier spacing is beneficial to obtain a small frame length with control overhead. They provided various techniques to reduce the growing guard period (GP) and cyclic prefix (CP) like complete utilization of multiple subcarrier spacing, management and data parts of frame at receiver end, various uses of timing advance (TA) or total control of flexible CP size.

This section includes various works that were done on 5G optimization by different authors. Table 11 shows how other authors worked on the improvement of multiple parameters such as energy efficiency, power optimization, and latency with 5G optimization.

Summary of Optimization Based Approaches in 5G Technology.

5. Description of Novel 5G Features over 4G

This section presents descriptions of various novel features of 5G, namely, the concept of small cell, beamforming, and MEC.

5.1. Small Cell

Small cells are low-powered cellular radio access nodes which work in the range of 10 meters to a few kilometers. Small cells play a very important role in implementation of the 5G wireless network. Small cells are low power base stations which cover small areas. Small cells are quite similar with all the previous cells used in various wireless networks. However, these cells have some advantages like they can work with low power and they are also capable of working with high data rates. Small cells help in rollout of 5G network with ultra high speed and low latency communication. Small cells in the 5G network use some new technologies like MIMO, beamforming, and mmWave for high speed data transmission. The design of small cells hardware is very simple so its implementation is quite easier and faster. There are three types of small cell tower available in the market. Femtocells, picocells, and microcells [ 83 ]. As shown in the Table 12 .

Types of Small cells.

MmWave is a very high band spectrum between 30 to 300 GHz. As it is a significantly less used spectrum, it provides very high-speed wireless communication. MmWave offers ultra-wide bandwidth for next-generation mobile networks. MmWave has lots of advantages, but it has some disadvantages, too, such as mmWave signals are very high-frequency signals, so they have more collision with obstacles in the air which cause the signals loses energy quickly. Buildings and trees also block MmWave signals, so these signals cover a shorter distance. To resolve these issues, multiple small cell stations are installed to cover the gap between end-user and base station [ 18 ]. Small cell covers a very shorter range, so the installation of a small cell depends on the population of a particular area. Generally, in a populated place, the distance between each small cell varies from 10 to 90 meters. In the survey [ 20 ], various authors implemented small cells with massive MIMO simultaneously. They also reviewed multiple technologies used in 5G like beamforming, small cell, massive MIMO, NOMA, device to device (D2D) communication. Various problems like interference management, spectral efficiency, resource management, energy efficiency, and backhauling are discussed. The author also gave a detailed presentation of all the issues occurring while implementing small cells with various 5G technologies. As shown in the Figure 7 , mmWave has a higher range, so it can be easily blocked by the obstacles as shown in Figure 7 a. This is one of the key concerns of millimeter-wave signal transmission. To solve this issue, the small cell can be placed at a short distance to transmit the signals easily, as shown in Figure 7 b.

Pictorial representation of communication with and without small cells.

5.2. Beamforming

Beamforming is a key technology of wireless networks which transmits the signals in a directional manner. 5G beamforming making a strong wireless connection toward a receiving end. In conventional systems when small cells are not using beamforming, moving signals to particular areas is quite difficult. Beamforming counter this issue using beamforming small cells are able to transmit the signals in particular direction towards a device like mobile phone, laptops, autonomous vehicle and IoT devices. Beamforming is improving the efficiency and saves the energy of the 5G network. Beamforming is broadly divided into three categories: Digital beamforming, analog beamforming and hybrid beamforming. Digital beamforming: multiuser MIMO is equal to digital beamforming which is mainly used in LTE Advanced Pro and in 5G NR. In digital beamforming the same frequency or time resources can be used to transmit the data to multiple users at the same time which improves the cell capacity of wireless networks. Analog Beamforming: In mmWave frequency range 5G NR analog beamforming is a very important approach which improves the coverage. In digital beamforming there are chances of high pathloss in mmWave as only one beam per set of antenna is formed. While the analog beamforming saves high pathloss in mmWave. Hybrid beamforming: hybrid beamforming is a combination of both analog beamforming and digital beamforming. In the implementation of MmWave in 5G network hybrid beamforming will be used [ 84 ].

Wireless signals in the 4G network are spreading in large areas, and nature is not Omnidirectional. Thus, energy depletes rapidly, and users who are accessing these signals also face interference problems. The beamforming technique is used in the 5G network to resolve this issue. In beamforming signals are directional. They move like a laser beam from the base station to the user, so signals seem to be traveling in an invisible cable. Beamforming helps achieve a faster data rate; as the signals are directional, it leads to less energy consumption and less interference. In [ 21 ], investigators evolve some techniques which reduce interference and increase system efficiency of the 5G mobile network. In this survey article, the authors covered various challenges faced while designing an optimized beamforming algorithm. Mainly focused on different design parameters such as performance evaluation and power consumption. In addition, they also described various issues related to beamforming like CSI, computation complexity, and antenna correlation. They also covered various research to cover how beamforming helps implement MIMO in next-generation mobile networks [ 85 ]. Figure 8 shows the pictorial representation of communication with and without using beamforming.

Pictorial Representation of communication with and without using beamforming.

5.3. Mobile Edge Computing

Mobile Edge Computing (MEC) [ 24 ]: MEC is an extended version of cloud computing that brings cloud resources closer to the end-user. When we talk about computing, the very first thing that comes to our mind is cloud computing. Cloud computing is a very famous technology that offers many services to end-user. Still, cloud computing has many drawbacks. The services available in the cloud are too far from end-users that create latency, and cloud user needs to download the complete application before use, which also increases the burden to the device [ 86 ]. MEC creates an edge between the end-user and cloud server, bringing cloud computing closer to the end-user. Now, all the services, namely, video conferencing, virtual software, etc., are offered by this edge that improves cloud computing performance. Another essential feature of MEC is that the application is split into two parts, which, first one is available at cloud server, and the second is at the user’s device. Therefore, the user need not download the complete application on his device that increases the performance of the end user’s device. Furthermore, MEC provides cloud services at very low latency and less bandwidth. In [ 23 , 87 ], the author’s investigation proved that successful deployment of MEC in 5G network increases the overall performance of 5G architecture. Graphical differentiation between cloud computing and mobile edge computing is presented in Figure 9 .

Pictorial representation of cloud computing vs. mobile edge computing.

6. 5G Security

Security is the key feature in the telecommunication network industry, which is necessary at various layers, to handle 5G network security in applications such as IoT, Digital forensics, IDS and many more [ 88 , 89 ]. The authors [ 90 ], discussed the background of 5G and its security concerns, challenges and future directions. The author also introduced the blockchain technology that can be incorporated with the IoT to overcome the challenges in IoT. The paper aims to create a security framework which can be incorporated with the LTE advanced network, and effective in terms of cost, deployment and QoS. In [ 91 ], author surveyed various form of attacks, the security challenges, security solutions with respect to the affected technology such as SDN, Network function virtualization (NFV), Mobile Clouds and MEC, and security standardizations of 5G, i.e., 3GPP, 5GPPP, Internet Engineering Task Force (IETF), Next Generation Mobile Networks (NGMN), European Telecommunications Standards Institute (ETSI). In [ 92 ], author elaborated various technological aspects, security issues and their existing solutions and also mentioned the new emerging technological paradigms for 5G security such as blockchain, quantum cryptography, AI, SDN, CPS, MEC, D2D. The author aims to create new security frameworks for 5G for further use of this technology in development of smart cities, transportation and healthcare. In [ 93 ], author analyzed the threats and dark threat, security aspects concerned with SDN and NFV, also their Commercial & Industrial Security Corporation (CISCO) 5G vision and new security innovations with respect to the new evolving architectures of 5G [ 94 ].

AuthenticationThe identification of the user in any network is made with the help of authentication. The different mobile network generations from 1G to 5G have used multiple techniques for user authentication. 5G utilizes the 5G Authentication and Key Agreement (AKA) authentication method, which shares a cryptographic key between user equipment (UE) and its home network and establishes a mutual authentication process between the both [ 95 ].

Access Control To restrict the accessibility in the network, 5G supports access control mechanisms to provide a secure and safe environment to the users and is controlled by network providers. 5G uses simple public key infrastructure (PKI) certificates for authenticating access in the 5G network. PKI put forward a secure and dynamic environment for the 5G network. The simple PKI technique provides flexibility to the 5G network; it can scale up and scale down as per the user traffic in the network [ 96 , 97 ].

Communication Security 5G deals to provide high data bandwidth, low latency, and better signal coverage. Therefore secure communication is the key concern in the 5G network. UE, mobile operators, core network, and access networks are the main focal point for the attackers in 5G communication. Some of the common attacks in communication at various segments are Botnet, message insertion, micro-cell, distributed denial of service (DDoS), and transport layer security (TLS)/secure sockets layer (SSL) attacks [ 98 , 99 ].

Encryption The confidentiality of the user and the network is done using encryption techniques. As 5G offers multiple services, end-to-end (E2E) encryption is the most suitable technique applied over various segments in the 5G network. Encryption forbids unauthorized access to the network and maintains the data privacy of the user. To encrypt the radio traffic at Packet Data Convergence Protocol (PDCP) layer, three 128-bits keys are applied at the user plane, nonaccess stratum (NAS), and access stratum (AS) [ 100 ].

7. Summary of 5G Technology Based on Above-Stated Challenges

In this section, various issues addressed by investigators in 5G technologies are presented in Table 13 . In addition, different parameters are considered, such as throughput, latency, energy efficiency, data rate, spectral efficiency, fairness & computing capacity, transmission rate, coverage, cost, security requirement, performance, QoS, power optimization, etc., indexed from R1 to R14.

Summary of 5G Technology above stated challenges (R1:Throughput, R2:Latency, R3:Energy Efficiency, R4:Data Rate, R5:Spectral efficiency, R6:Fairness & Computing Capacity, R7:Transmission Rate, R8:Coverage, R9:Cost, R10:Security requirement, R11:Performance, R12:Quality of Services (QoS), R13:Power Optimization).

8. Conclusions

This survey article illustrates the emergence of 5G, its evolution from 1G to 5G mobile network, applications, different research groups, their work, and the key features of 5G. It is not just a mobile broadband network, different from all the previous mobile network generations; it offers services like IoT, V2X, and Industry 4.0. This paper covers a detailed survey from multiple authors on different technologies in 5G, such as massive MIMO, Non-Orthogonal Multiple Access (NOMA), millimeter wave, small cell, MEC (Mobile Edge Computing), beamforming, optimization, and machine learning in 5G. After each section, a tabular comparison covers all the state-of-the-research held in these technologies. This survey also shows the importance of these newly added technologies and building a flexible, scalable, and reliable 5G network.

9. Future Findings

This article covers a detailed survey on the 5G mobile network and its features. These features make 5G more reliable, scalable, efficient at affordable rates. As discussed in the above sections, numerous technical challenges originate while implementing those features or providing services over a 5G mobile network. So, for future research directions, the research community can overcome these challenges while implementing these technologies (MIMO, NOMA, small cell, mmWave, beam-forming, MEC) over a 5G network. 5G communication will bring new improvements over the existing systems. Still, the current solutions cannot fulfill the autonomous system and future intelligence engineering requirements after a decade. There is no matter of discussion that 5G will provide better QoS and new features than 4G. But there is always room for improvement as the considerable growth of centralized data and autonomous industry 5G wireless networks will not be capable of fulfilling their demands in the future. So, we need to move on new wireless network technology that is named 6G. 6G wireless network will bring new heights in mobile generations, as it includes (i) massive human-to-machine communication, (ii) ubiquitous connectivity between the local device and cloud server, (iii) creation of data fusion technology for various mixed reality experiences and multiverps maps. (iv) Focus on sensing and actuation to control the network of the entire world. The 6G mobile network will offer new services with some other technologies; these services are 3D mapping, reality devices, smart homes, smart wearable, autonomous vehicles, artificial intelligence, and sense. It is expected that 6G will provide ultra-long-range communication with a very low latency of 1 ms. The per-user bit rate in a 6G wireless network will be approximately 1 Tbps, and it will also provide wireless communication, which is 1000 times faster than 5G networks.

Acknowledgments

Author contributions.

Conceptualization: R.D., I.Y., G.C., P.L. data gathering: R.D., G.C., P.L, I.Y. funding acquisition: I.Y. investigation: I.Y., G.C., G.P. methodology: R.D., I.Y., G.C., P.L., G.P., survey: I.Y., G.C., P.L, G.P., R.D. supervision: G.C., I.Y., G.P. validation: I.Y., G.P. visualization: R.D., I.Y., G.C., P.L. writing, original draft: R.D., I.Y., G.C., P.L., G.P. writing, review, and editing: I.Y., G.C., G.P. All authors have read and agreed to the published version of the manuscript.

This paper was supported by Soonchunhyang University.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Help | Advanced Search

Computer Science > Information Theory

Title: from multilayer perceptron to gpt: a reflection on deep learning research for wireless physical layer.

Abstract: Most research studies on deep learning (DL) applied to the physical layer of wireless communication do not put forward the critical role of the accuracy-generalization trade-off in developing and evaluating practical algorithms. To highlight the disadvantage of this common practice, we revisit a data decoding example from one of the first papers introducing DL-based end-to-end wireless communication systems to the research community and promoting the use of artificial intelligence (AI)/DL for the wireless physical layer. We then put forward two key trade-offs in designing DL models for communication, namely, accuracy versus generalization and compression versus latency. We discuss their relevance in the context of wireless communications use cases using emerging DL models including large language models (LLMs). Finally, we summarize our proposed evaluation guidelines to enhance the research impact of DL on wireless communications. These guidelines are an attempt to reconcile the empirical nature of DL research with the rigorous requirement metrics of wireless communications systems.

Submission history

Access paper:.

• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

BibTeX formatted citation

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Futurity is your source of research news from leading universities.

• About Futurity
• Universities
• Environment

New tech could lead to smaller, stronger wireless devices

In Matt Eichenfield's lab at Sandia National Laboratories, he and his team use multiple microwave frequencies to characterize a nonlinear phononic mixing device they built on a silicon wafer. (Credit: Bret Latter/Sandia National Laboratories)

You are free to share this article under the Attribution 4.0 International license.

• mobile devices
• wireless technology

A new class of synthetic materials could lead to the next revolution of  wireless technologies, enabling devices to be smaller, require less signal strength, and use less power.

The key to these advances lies in what experts call phononics , which is similar to photonics. Both take advantage of similar physical laws and offer new ways to advance technology.

While photonics takes advantage of photons—or light—phononics does the same with phonons, which are the physical particles that transmit mechanical vibrations through a material, akin to sound, but at frequencies much too high to hear.

In their paper in Nature Materials , researchers report clearing a major milestone toward real-world applications based on phononics.

By combining highly specialized semiconductor materials and piezoelectric materials not typically used together, the researchers were able to generate giant nonlinear interactions between phonons. Together with previous innovations demonstrating amplifiers for phonons using the same materials, this opens up the possibility of making wireless devices such as smartphones or other data transmitters smaller, more efficient, and more powerful.

“Most people would probably be surprised to hear that there are something like 30 filters inside their cell phone whose sole job it is to transform radio waves into sound waves and back,” says senior author Matt Eichenfield, who holds a joint appointment at the University of Arizona College of Optical Sciences and Sandia National Laboratories in Albuquerque, New Mexico.

Part of what are known as front-end processors, these piezoelectric filters, made on special microchips, are necessary to convert sound and electronic waves multiple times each time a smartphone receives or sends data, he says.

Because these can’t be made out of the same materials, such as silicon, as the other critically important chips in the front-end processor, the physical size of your device is much bigger than it needs to be, and along the way, there are losses from going back and forth between radio waves and sound waves that add up and degrade the performance, Eichenfield says.

“Normally, phonons behave in a completely linear fashion, meaning they don’t interact with each other,” he says. “It’s a bit like shining one laser pointer beam through another; they just go through each other.”

Nonlinear phononics refers to what happens in special materials when the phonons can and do interact with each other, Eichenfield says. In the paper, the researchers demonstrated what he calls “giant phononic nonlinearities.” The synthetic materials produced by the research team caused the phonons to interact with each other much more strongly than in any conventional material.

“In the laser pointer analogy, this would be like changing the frequency of the photons in the first laser pointer when you turn on the second,” he says. “As a result, you’d see the beam from the first one changing color.”

With the new phononics materials, the researchers demonstrated that one beam of phonons can, in fact, change the frequency of another beam. What’s more, they showed that phonons can be manipulated in ways that could only be realized with transistor-based electronics—until now.

The group has been working toward the goal of making all of the components needed for radio frequency signal processors using acoustic wave technologies instead of transistor-based electronics on a single chip, in a way that’s compatible with standard microprocessor manufacturing, and the latest publication proves that it can be done.

Previously, the researchers succeeded in making acoustic components including amplifiers, switches, and others. With the acoustic mixers described in the latest publication, they have added the last piece of the puzzle.

“Now, you can point to every component in a diagram of a radiofrequency front-end processor and say, ‘Yeah, I can make all of these on one chip with acoustic waves,'” Eichenfield says. “We’re ready to move on to making the whole shebang in the acoustic domain.”

Having all the components needed to make a radio frequency front end on a single chip could shrink devices such as cell phones and other wireless communication gadgets by as much as a factor of a 100, according to Eichenfield.

The team accomplished its proof of principle by combining highly specialized materials into microelectronics-sized devices through which they sent acoustic waves. Specifically, they took a silicon wafer with a thin layer of lithium niobite—a synthetic material used extensively in piezoelectronic devices and cell phones—and added an ultra-thin layer (fewer than 100 atoms thick) of a semiconductor containing indium gallium arsenide.

“When we combined these materials in just the right way, we were able to experimentally access a new regime of phononic nonlinearity,” says lead author Lisa Hackett, an engineer at Sandia National Laboratories. “This means we have a path forward to inventing high-performance tech for sending and receiving radio waves that’s smaller than has ever been possible.”

In this setup, acoustic waves moving through the system behave in nonlinear ways when they travel through the materials. This effect can be used to change frequencies and encode information.

A staple of photonics, nonlinear effects have long been used to make things like invisible laser light into visible laser pointers, but taking advantage of nonlinear effects in phononics has been hindered by limitations in technology and materials. For example, while lithium niobate is one of the most nonlinear phononic materials known, its usefulness for technical applications is hindered by the fact that those nonlinearities are very weak when used on its own.

By adding the indium-gallium arsenide semiconductor, Eichenfield’s group created an environment in which the acoustic waves traveling through the material influence the distribution of electrical charges in the indium gallium arsenide semiconductor film, causing the acoustic waves to mix in specific ways that can be controlled, opening up the system to various applications.

“The effective nonlinearity you can generate with these materials is hundreds or even thousands of times larger than was possible before, which is crazy,” Eichenfield says. “If you could do the same for nonlinear optics, you would revolutionize the field.”

With physical size being one of the fundamental limitations of current, state-of-the-art radiofrequency processing hardware, the new technology could open the door to electronic devices that are even more capable than their current counterparts, according to the authors.

Communication devices that take virtually no space, have better signal coverage, and longer battery life, are on the horizon.

Source: University of Arizona

Is ‘curving’ light the secret to better wireless communication?

Super thin heat shield lets your phone keep its cool, wireless tech activates fly brain circuits in under a second, stay connected. subscribe to our newsletter..

Add your information below to receive daily updates.

• Phone This field is for validation purposes and should be left unchanged.

NTRS - NASA Technical Reports Server

Available downloads, related records.

LEO/VLEO Satellite Communications in 6G and Beyond Networks – Technologies, Applications and Challenges

Ieee account.

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Beamforming Techniques for MIMO-NOMA for 5G and Beyond 5G: Research Gaps and Future Directions

• Published: 02 November 2023
• Volume 43 , pages 1518–1548, ( 2024 )

Cite this article

• Sadiq Ur Rehman 1 ,
• Jawwad Ahmad   ORCID: orcid.org/0000-0002-7138-6452 2 ,
• Anwaar Manzar 1 &
• Muhammad Moinuddin 3 , 4  

238 Accesses

2 Citations

Explore all metrics

Effective sharing of the communication channel among many users, or multiple access (MA) techniques, can play a vital role in meeting the diverse demands of low latency, high reliability, massive connectivity, better fairness, and high throughput. In this context, non-orthogonal multiple access with multiple antennas, also known as multiple-input, multiple-output NOMA (MIMO-NOMA), is a promising enabling technology for fifth-generation (5G) and beyond (5G) wireless networks. The proper design of beamforming systems is one of the major difficulties in developing MIMO-NOMA. There are various ways to design beamforming for MIMO-NOMA in the literature. However, there is not much work dedicated to the survey focusing only on beamforming design in MIMO-NOMA systems. This work presents a comprehensive overview of beamforming methods in MIMO-NOMA for 5G and B5G. These strategies are classified in detail and have varied attributes, benefits, and drawbacks. As a result, future research gaps are also highlighted. Moreover, a simulation study is presented as a case study on the impact of random beamforming in various scenarios of heterogeneous environments with small and macro-cells. For this purpose, users’ outage probability is simulated with various types of interference in the heterogeneous systems, including inter-cluster, cross-tier, and co-tier interferences. This analysis also helps to contrast the performance of small and macro-cells. Finally, future research directions are discussed for beamforming in MIMO-NOMA for 5G and B5G.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Application of cell-free massive MIMO in 5G and beyond 5G wireless networks: a survey

Joint power optimization and scaled beamforming approach in B5G network based massive MIMO enabled HetNet with full-duplex small cells

CMBF-based dynamic selection for heterogeneous massive MIMO systems

Availability of data and materials.

There are no data associated with this manuscript.

A. Ahmed, Z. Elsaraf, F.A. Khan, Q.Z. Ahmed, Cooperative non-orthogonal multiple access for beyond 5G networks. IEEE Open J. Commun. Soc. 2 , 990–999 (2021)

Article   Google Scholar  

W.A. Al-Hussaibi, F.H. Ali, Efficient user clustering, receive antenna selection, and power allocation algorithms for massive MIMO-NOMA systems. IEEE Access 7 , 31865–31882 (2019)

H.M. Al-Obiedollah, K. Cumanan, J. Thiyagalingam, A.G. Burr, Z. Ding, O.A. Dobre, Energy efficient beamforming design for miso non-orthogonal multiple access systems. IEEE Trans. Commun. 67 (6), 4117–4131 (2019)

H.M. Al-Obiedollah, K. Cumanan, J. Thiyagalingam, J. Tang, A.G. Burr, Z. Ding, O.A. Dobre, Spectral-energy efficiency trade-off-based beamforming design for miso non-orthogonal multiple access systems. IEEE Trans. Wirel. Commun. 19 (10), 6593–6606 (2020)

M.S. Ali, E. Hossain, A. Al-Dweik, D.I. Kim, Downlink power allocation for COMP-NOMA in multi-cell networks. IEEE Trans. Commun. 66 (9), 3982–3998 (2018)

S. Ali, E. Hossain, D.I. Kim, Non-orthogonal multiple access (NOMA) for downlink multiuser MIMO systems: user clustering, beamforming, and power allocation. IEEE Access 5 , 565–577 (2016)

S.H. Amin, A.H. Mehana, S.S. Soliman, Y.A. Fahmy, Power allocation for maximum MIMO-NOMA system user-rate, in 2018 IEEE Globecom Workshops (GC Wkshps) (IEEE, 2018), pp. 1–6

A. Araghi, M. Khalily, M. Safaei, A. Bagheri, V. Singh, F. Wang, R. Tafazolli, Reconfigurable intelligent surface (RIS) in the sub-6 GHz band: design, implementation, and real-world demonstration. IEEE Access 10 , 2646–2655 (2022)

K. Aswini, M. Surendar, Capacity analysis of intelligent reflecting surface assisted RSMA system with perfect and imperfect CSI for 6G. J. Phys. Conf. Ser. 2466 , 012001 (2023)

F.A. Azhiri, B.M. Tazehkand, R. Abdolee, A novel EO-based optimum random beamforming method in mmWave-NOMA systems with sparse antenna array. Digit. Commun. Netw. (2023). https://doi.org/10.1016/j.dcan.2023.02.010

V.L. Babu, L. Mathews, S.S. Pillai, Performance analysis of linear and nonlinear precoding in MIMO systems. Int. J. Adv. Res. Comput. Commun. Eng. 4 (6), 373–376 (2015)

Google Scholar  

İ Baştürk, Evaluation of energy-efficiency problem in orthogonal frequency division multiple access cellular networks. Celal Bayar Univ. J. Sci. 15 (1), 9–15 (2019)

MathSciNet   Google Scholar  

Y. Bo, J. Fonseka, Throughput enhancement on the downlink of 4G and 5G systems: NOMA, BOMA and IBOMA. Int. J. Sens. Wirel. Commun. Control 8 (1), 57–64 (2018)

R.M. Buehrer, Code Division Multiple Access (CDMA) (Springer, Berlin, 2022)

B.P. Chaudhary, R. Shankar, R.K. Mishra, A tutorial on cooperative non-orthogonal multiple access networks. J. Defense Model. Simul. 19 (4), 563–573 (2022)

C. Chen, W. Cai, X. Cheng, L. Yang, Y. Jin, Low complexity beamforming and user selection schemes for 5G MIMO-NOMA systems. IEEE J. Sel. Areas Commun. 35 (12), 2708–2722 (2017)

X. Chen, D.W.K. Ng, W. Yu, E.G. Larsson, N. Al-Dhahir, R. Schober, Massive access for 5G and beyond. IEEE J. Sel. Areas Commun. 39 (3), 615–637 (2020)

Y. Cheng, K.H. Li, Y. Liu, K.C. Teh, H.V. Poor, Downlink and uplink intelligent reflecting surface aided networks: NOMA and OMA. IEEE Trans. Wirel. Commun. 20 (6), 3988–4000 (2021)

S. Chinnadurai, P. Selvaprabhu, Y. Jeong, X. Jiang, M.H. Lee, Worst-case energy efficiency maximization in a 5G massive MIMO-NOMA system. Sensors 17 (9), 2139 (2017)

Article   ADS   PubMed   PubMed Central   Google Scholar  

S. Chinnadurai, P. Selvaprabhu, Y. Jeong, A.L. Sarker, H. Hai, W. Duan, M.H. Lee, User clustering and robust beamforming design in multicell MIMO-NOMA system for 5G communications. AEU-Int. J. Electron. Commun. 78 , 181–191 (2017)

S. Chinnadurai, P. Selvaprabhu, M.H. Lee, A novel joint user pairing and dynamic power allocation scheme in MIMO-NOMA system, in 2017 International Conference on Information and Communication Technology Convergence (ICTC) (IEEE, 2017), pp. 951–953

K.H. Chung, On improved outage probability of correlated superposition coding/non-SIC NOMA. J. Korea Inst. Electron. Commun. Sci. 16 (4), 611–616 (2021)

J. Cui, Z. Ding, P. Fan, Power minimization strategies in downlink MIMO-NOMA systems, in 2017 IEEE International Conference on Communications (ICC) (IEEE, 2017), pp. 1–6

X. Dai, Z. Zhang, B. Bai, S. Chen, S. Sun, Pattern division multiple access: a new multiple access technology for 5G. IEEE Wirel. Commun. 25 (2), 54–60 (2018)

Article   ADS   Google Scholar  

O. Elijah, S.K.A. Rahim, W.K. New, C.Y. Leow, K. Cumanan, T.K. Geok, Intelligent massive MIMO systems for beyond 5G networks: an overview and future trends. IEEE Access 10 , 102532 (2022)

N. Estrada Brito, C. Morales Alarcón, Non-orthogonal multiple access technique performance in a long-term evolution mobile communication system. Revista Digital Novasinergia 3 (1), 62–76 (2020)

J.L. Frauendorf, É. Almeida de Souza: Aas—advanced antenna system: the MIMO, massive MIMO, and beamforming antennas, in The Architectural and Technological Revolution of 5G (Springer, 2022), pp. 69–81

M. Ghous, A.K. Hassan, Z.H. Abbas, G. Abbas, A. Hussien, T. Baker, Cooperative power-domain NOMA systems: an overview. Sensors 22 (24), 9652 (2022)

L.C. Godara, Applications of antenna arrays to mobile communications. I. Performance improvement, feasibility, and system considerations. Proc. IEEE 85 (7), 1031–1060 (1997)

K. Higuchi, Y. Kishiyama, Non-orthogonal access with random beamforming and intra-beam SIC for cellular MIMO downlink, in 2013 IEEE 78th Vehicular Technology Conference (VTC Fall) (IEEE, 2013), pp. 1–5

R. Hoshyar, R. Razavi, M. Al-Imari, LDS-OFDM an efficient multiple access technique, in 2010 IEEE 71st Vehicular Technology Conference (IEEE, 2010), pp. 1–5

Y. Hu, L. Ping, Interleave division multiple access (IDMA), in Multiple Access Techniques for 5G Wireless Networks and Beyond (2019), pp. 417–449

H. Huang, Y. Shi, L. Liang, J. He, X. Zhang, Performance analysis of overlay cognitive NOMA network with imperfect SIC and imperfect CSI. Phys. Commun. 53 , 101711 (2022)

S.R. Islam, M. Zeng, O.A. Dobre, K.S. Kwak, Resource allocation for downlink NOMA systems: key techniques and open issues. IEEE Wirel. Commun. 25 (2), 40–47 (2018)

N. Iswarya, L. Jayashree, A survey on successive interference cancellation schemes in non-orthogonal multiple access for future radio access. Wirel. Pers. Commun. 120 (2), 1057–1078 (2021)

Joe: NOMA vs OMA—capacity comparison (2020). https://ecewireless.blogspot.com/2020/09/noma-vs-oma-capacity-comparison.html . Accessed 15 Jul 2023

V. Jungnickel, K. Manolakis, W. Zirwas, B. Panzner, V. Braun, M. Lossow, M. Sternad, R. Apelfröjd, T. Svensson, The role of small cells, coordinated multipoint, and massive MIMO in 5G. IEEE Commun. Mag. 52 (5), 44–51 (2014)

M. Kaliszan, E. Pollakis, S. Stańczak, Multigroup multicast with application-layer coding: Beamforming for maximum weighted sum rate, in 2012 IEEE Wireless Communications and Networking Conference (WCNC) (IEEE, 2012), pp. 2270–2275

P.R. Kapula, P. Sridevi, Channel estimation in 5G multi input multi output wireless communication using optimized deep neural framework. Clust. Comput. 25 (5), 3517–3530 (2022)

F. Kara, H. Kaya, BER performances of downlink and uplink NOMA in the presence of SIC errors over fading channels. IET Commun. 12 (15), 1834–1844 (2018)

B.A. Karim, H.K. Ali, A novel beamforming technique using mmWave antenna arrays for 5G wireless communication networks. Digit. Signal Process. 134 , 103917 (2023)

R. Kataoka, I. Kanno, T. Hayashi, N. Tsumachi, T. Suzuki, H. Ishikawa, K. Yamazaki, Y. Kishi, Outdoor experimental evaluation of asynchronous successive interference cancellation for 5G in shared spectrum with different radio systems. IEICE Commun. Express 10 (8), 587–592 (2021)

W.U. Khan, M.A. Jamshed, E. Lagunas, S. Chatzinotas, X. Li, B. Ottersten, Energy efficiency optimization for backscatter enhanced NOMA cooperative V2X communications under imperfect CSI. IEEE Trans. Intell. Transp. Syst. (2022)

B. Kimy, S. Lim, H. Kim, S. Suh, J. Kwun, S. Choi, C. Lee, S. Lee, D. Hong, Non-orthogonal multiple access in a downlink multiuser beamforming system, in MILCOM 2013-2013 IEEE Military Communications Conference (IEEE, 2013), pp. 1278–1283

A. Koohang, C.S. Sargent, J.H. Nord, J. Paliszkiewicz, Internet of things (IoT): from awareness to continued use. Int. J. Inf. Manag. 62 , 102442 (2022)

K. Lakshmanna, R. Kaluri, N. Gundluru, Z.S. Alzamil, D.S. Rajput, A.A. Khan, M.A. Haq, A. Alhussen, A review on deep learning techniques for IoT data. Electronics 11 (10), 1604 (2022)

Q.N. Le, V.D. Nguyen, O.A. Dobre, N.P. Nguyen, R. Zhao, S. Chatzinotas, Learning-assisted user clustering in cell-free massive MIMO-NOMA networks. IEEE Trans. Veh. Technol. 70 (12), 12872–12887 (2021)

B.K.S. Lima, A.S. De Sena, R. Dinis, D.B. Da Costa, M. Beko, R. Oliveira, M. Debbah, Aerial intelligent reflecting surfaces in MIMO-NOMA networks: fundamentals, potential achievements, and challenges. IEEE Open J. Commun. Soc. 3 , 1007–1024 (2022)

S. Liu, C. Zhang, Non-orthogonal multiple access in a downlink multiuser beamforming system with limited CSI feedback. EURASIP J. Wirel. Commun. Netw. 2016 (1), 1–11 (2016)

Article   MathSciNet   Google Scholar  

Y. Liu, S. Zhang, X. Mu, Z. Ding, R. Schober, N. Al-Dhahir, E. Hossain, X. Shen, Evolution of NOMA toward next generation multiple access (NGMA) for 6G. IEEE J. Sel. Areas Commun. 40 (4), 1037–1071 (2022)

Article   CAS   Google Scholar  

Z. Liu, P. Xiao, Z. Mheich, Power-imbalanced low-density signatures (LDS) from Eisenstein numbers, in 2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS) (IEEE, 2019), pp. 1–5

Z. Liu, L.L. Yang, Sparse or dense: a comparative study of code-domain NOMA systems. IEEE Trans. Wirel. Commun. 20 (8), 4768–4780 (2021)

D. López-Pérez, A. De Domenico, N. Piovesan, G. Xinli, H. Bao, S. Qitao, M. Debbah, A survey on 5G radio access network energy efficiency: massive MIMO, lean carrier design, sleep modes, and machine learning. IEEE Commun. Surv. Tutor. 24 (1), 653–697 (2022)

B. Lu, S. Lin, J. Shi, TDMA-NOMA based computation offloading for cognitive capacity harvesting networks with transmission order optimization. IEEE Trans. Commun. 70 (9), 6355–6369 (2022)

Y. Lu, Y. Qu, C. Yang, T. Li, X. Wang, H. Bian, H. Zhu, A user matching and power allocation scheme for downlink MIMO-NOMA communication system. Phys. Commun. 42 , 101174 (2020)

M.R. Mahmood, M.A. Matin, P. Sarigiannidis, S.K. Goudos, G.K. Karagiannidis, Residual compensation-based extreme learning machine for MIMO-NOMA receiver. IEEE Access 11 , 13398–13407 (2023)

M. Mhedhbi, F.E. Boukour, Analysis and evaluation of pattern division multiple access scheme jointed with 5G waveforms. IEEE Access 7 , 21826–21833 (2019)

A. Misra, U.R. Kalita, K.K. Sarma, Performance analysis of multi user MIMO NOMA network with hierarchical clustering. Int. J. Electron. Lett. (2022). https://doi.org/10.1080/21681724.2022.2129810

P. Mursia, I. Atzeni, D. Gesbert, L. Cottatellucci, Covariance shaping for massive MIMO systems, in 2018 IEEE Global Communications Conference (GLOBECOM) (IEEE, 2018), pp. 1–6

H.G. Myung, J. Lim, D.J. Goodman, Single carrier FDMA for uplink wireless transmission. IEEE Veh. Technol. Mag. 1 (3), 30–38 (2006)

G. Naik, J.M. Park, J. Ashdown, W. Lehr, Next generation Wi-Fi and 5G NR-U in the 6 GHz bands: opportunities and challenges. IEEE Access 8 , 153027–153056 (2020)

A. Nasser, O. Muta, M. Elsabrouty, H. Gacanin, Interference mitigation and power allocation scheme for downlink MIMO-NOMA HetNet. IEEE Trans. Veh. Technol. 68 (7), 6805–6816 (2019)

S. Norouzi, B. Champagne, Y. Cai, Joint optimization framework for user clustering, downlink beamforming, and power allocation in MIMO NOMA systems. IEEE Trans. Commun. 71 (1), 214–228 (2022)

H. Pan, S.C. Liew, Information update: TDMA or FDMA? IEEE Wirel. Commun. Lett. 9 (6), 856–860 (2020)

P. Pirinen, A brief overview of 5G research activities, in 1st International Conference on 5G for Ubiquitous Connectivity (IEEE, 2014), pp. 17–22

B.U. Rehman, M.I. Babar, G.A. Azim, M. Amir, H. Alhumyani, M.S. Alzaidi, M. Alshammari, R. Saeed, Uplink power control scheme for spectral efficiency maximization in NOMA systems. Alex. Eng. J. 64 , 667–677 (2023)

S.U. Rehman, J. Ahmad, A. Manzar, M. Moinuddin, Outage probability and ergodic capacity analysis of MIMO-NOMA heterogeneous network for 5G system. J. Indep. Stud. Res. Comput. 20 (2), 15–23 (2022)

S.U. Rehman, A. Hussain, F. Hussain, M.A. Mannan, A comprehensive study: 5G wireless networks and emerging technologies. Int. Electr. Eng. Conf. (IEEC) 5 , 25–32 (2020)

S.U. Rehman, H. Mustafa, A.R. Larik, IoT based substation monitoring & control system using Arduino with data logging, in 2021 4th International Conference on Computing & Information Sciences (ICCIS) (IEEE, 2021), pp. 1–6

S.U.R. Aqeel-ur Rehman, I.U. Khan, M. Moiz, S. Hasan, Security and privacy issues in IoT. Int. J. Commun. Netw. Inf. Secur. (IJCNIS) 8 (3), 147–157 (2016)

F. Rinaldi, A. Raschella, S. Pizzi, 5G NR system design: a concise survey of key features and capabilities. Wirel. Netw. 27 , 5173–5188 (2021)

S.K. Sa, A.K. Mishra, An uplink cooperative NOMA based on CDRT with hardware impairments and imperfect CSI. IEEE Syst. J. (2023). https://doi.org/10.1109/JSYST.2023.3275469

Y. Saito, Y. Kishiyama, A. Benjebbour, T. Nakamura, A. Li, K. Higuchi, Non-orthogonal multiple access (NOMA) for cellular future radio access, in 2013 IEEE 77th vehicular technology conference (VTC Spring) (IEEE, 2013), pp. 1–5

M. Sakai, K. Kamohara, H. Iura, H. Nishimoto, K. Ishioka, Y. Murata, M. Yamamoto, A. Okazaki, N. Nonaka, S. Suyama et al., Experimental field trials on MU-MIMO transmissions for high SHF wide-band massive MIMO in 5G. IEEE Trans. Wirel. Commun. 19 (4), 2196–2207 (2020)

T. Sanjana, M. Suma, Deep learning approaches used in downlink MIMO-NOMA system: a survey, in Soft Computing and Signal Processing: Proceedings of 3rd ICSCSP 2020 , vol. 1 (Springer, 2021), pp. 687–704

A. Sarin, A.T. Avestruz, A framework for code division multiple access wireless power transfer. IEEE Access 9 , 135079–135101 (2021)

M.A. Shaikh, A. Manzar, M. Moinuddin, S.U. Rehman, H. Mustafa, Semi-blind beamforming in beam space MIMO NOMA for mmWave communications. IEEE Access 10 , 120426–120435 (2022)

L. Shan, S. Gao, S. Chen, M. Xu, F. Zhang, X. Bao, M. Chen, Energy-efficient resource allocation in NOMA-integrated V2X networks. Comput. Commun. 197 , 23–33 (2023)

W. Shao, S. Zhang, X. Zhang, J. Ma, N. Zhao, Suppressing interference and power allocation over the multi-cell MIMO-NOMA networks. IEEE Commun. Lett. 23 (8), 1397–1400 (2019)

A. Sharma, R.K. Jha, A comprehensive survey on security issues in 5G wireless communication network using beamforming approach. Wirel. Pers. Commun. 119 (4), 3447–3501 (2021)

H. Sharma, N. Kumar, R. Tekchandani, Physical layer security using beamforming techniques for 5G and beyond networks: a systematic review. Phys. Commun. 54 , 101791 (2022)

P.K. Sharma, D. Sharma, E.K. Kumari, T.S. Rao, Performance analysis of non-orthogonal multiple access over orthogonal multiple access, in Wireless Communication with Artificial Intelligence (CRC Press), pp. 273–293

M. Shehab, T. Khattab, M. Kucukvar, D. Trinchero, The role of 5g/6g networks in building sustainable and energy-efficient smart cities, in 2022 IEEE 7th International Energy Conference (ENERGYCON) (IEEE, 2022), pp. 1–7

R. Shen, X. Wang, Y. Xu, Beamforming design for uplink multi-cell MIMO-NOMA systems, in 2019 IEEE 5th International Conference on Computer and Communications (ICCC) (IEEE, 2019), pp. 2039–2043

Z. Shi, H. Wang, Y. Fu, G. Yang, S. Ma, F. Hou, T.A. Tsiftsis, Zero-forcing-based downlink virtual MIMO-NOMA communications in IoT networks. IEEE Internet Things J. 7 (4), 2716–2737 (2019)

W. Shin, M. Vaezi, B. Lee, D.J. Love, J. Lee, H.V. Poor, Coordinated beamforming for multi-cell MIMO-NOMA. IEEE Commun. Lett. 21 (1), 84–87 (2016)

W. Shin, M. Vaezi, B. Lee, D.J. Love, J. Lee, H.V. Poor, Non-orthogonal multiple access in multi-cell networks: theory, performance, and practical challenges. IEEE Commun. Mag. 55 (10), 176–183 (2017)

Y. Shobha, H. Rangaraju, Design of novel approach for emerging power-domain superposition coding (SC)-using hybrid NOMA-OFDM for 5G communications. Int. J. Intell. Unmanned Syst. (ahead-of-print) (2021)

Z. Si, H. Wan, T. Qin, Z. Wang, RIS-aided cell-free MIMO system: perfect and imperfect CSI design for energy efficiency. IEICE Trans. Commun. (2023). https://doi.org/10.1587/transcom.2022EBP3173

N.D. Sidiropoulos, T.N. Davidson, Z.Q. Luo, Transmit beamforming for physical-layer multicasting. IEEE Trans. Signal Process. 54 (6), 2239–2251 (2006)

G. Singh, A. Srivastava, V.A. Bohara, Z. Liu, Downlink performance of optical power domain NOMA for beyond 5G enabled V2X networks. IEEE Open J. Veh. Technol. 2 , 235–248 (2021)

S. Solanki, J. Park, I. Lee, On the performance of IRS-aided UAV networks with NOMA. IEEE Trans. Veh. Technol. 71 (8), 9038–9043 (2022)

A.B. Tambawal, R.M. Noor, R. Salleh, C. Chembe, M.H. Anisi, O. Michael, J. Lloret, Time division multiple access scheduling strategies for emerging vehicular ad hoc network medium access control protocols: a survey. Telecommun. Syst. 70 , 595–616 (2019)

M. Tian, Q. Zhang, S. Zhao, Q. Li, J. Qin, Robust beamforming in downlink MIMO NOMA networks using cutting-set method. IEEE Commun. Lett. 22 (3), 574–577 (2017)

H. Tran, V.H. Dang, D. Niyato, D.N. Cuong, N.C. Luong, C. So-In et al., Outage probability minimization in secure NOMA cognitive radio systems with UAV relay: a machine learning approach. IEEE Trans. Cogn. Commun. Netw. 9 (2), 435–451 (2022)

A. Tusha, S. Doğan, H. Arslan, A hybrid downlink NOMA with OFDM and OFDM-IM for beyond 5G wireless networks. IEEE Signal Process. Lett. 27 , 491–495 (2020)

B. Wang, L. Dai, Z. Wang, N. Ge, S. Zhou, Spectrum and energy-efficient beamspace MIMO-NOMA for millimeter-wave communications using lens antenna array. IEEE J. Sel. Areas Commun. 35 (10), 2370–2382 (2017)

C.L. Wang, T.H. Liou, Improved modulation schemes for lattice-partition-based downlink non-orthogonal multiple access systems. IEEE Wirel. Commun. Lett. 9 (12), 2130–2134 (2020)

Q. Wang, T. Li, R. Feng, C. Yang, An efficient large resource-user scale SCMA codebook design method. IEEE Commun. Lett. 23 (10), 1787–1790 (2019)

F. Wei, W. Chen, Low complexity iterative receiver design for sparse code multiple access. IEEE Trans. Commun. 65 (2), 621–634 (2016)

C. Windpassinger, R.F. Fischer, T. Vencel, J.B. Huber, Precoding in multiantenna and multiuser communications. IEEE Trans. Wirel. Commun. 3 (4), 1305–1316 (2004)

Y. Wu, L.P. Qian, Energy-efficient NOMA-enabled traffic offloading via dual-connectivity in small-cell networks. IEEE Commun. Lett. 21 (7), 1605–1608 (2017)

Y. Wu, Y. Song, T. Wang, L. Qian, T.Q. Quek, Non-orthogonal multiple access assisted federated learning via wireless power transfer: a cost-efficient approach. IEEE Trans. Commun. 70 (4), 2853–2869 (2022)

K. Xiao, S. Dai, H. Rutagemwa, B. Rong, L. Gong, M. Kadoch, A novel opportunistic NOMA scheme for 5G massive MIMO multicast communications, in 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall) (IEEE, 2017), pp. 1–5

Z. Xiao, Z. Han, A. Nallanathan, O.A. Dobre, B. Clerckx, J. Choi, C. He, W. Tong, Antenna array enabled space/air/ground communications and networking for 6G. IEEE J. Sel. Areas Commun. 40 (10), 2773–2804 (2022)

Z. Xie, W. Yi, X. Wu, Y. Liu, A. Nallanathan, Modeling and coverage analysis for RIS-aided NOMA transmissions in heterogeneous networks. arXiv preprint arXiv:2104.13182 (2021)

L. Yang, Y. Liu, Y. Siu, Low complexity message passing algorithm for SCMA system. IEEE Commun. Lett. 20 (12), 2466–2469 (2016)

X. Yue, Y. Liu, Performance analysis of intelligent reflecting surface assisted NOMA networks. IEEE Trans. Wirel. Commun. 21 (4), 2623–2636 (2021)

X. Yue, Z. Qin, Y. Liu, S. Kang, Y. Chen, A unified framework for non-orthogonal multiple access. IEEE Trans. Commun. 66 (11), 5346–5359 (2018)

C.H. Yuen, P. Amini, B. Farhang-Boroujeny, Single carrier frequency division multiple access (SC-FDMA) for filter bank multicarrier communication systems, in 2010 Proceedings of the Fifth International Conference on Cognitive Radio Oriented Wireless Networks and Communications (IEEE, 2010), pp. 1–5

M. Zeeshan, K. Shahzad, M.U. Farooq, Noma-enabled cognitive communication based on hybrid narrowband/wideband SDR waveform, in 2022 3rd International Informatics and Software Engineering Conference (IISEC) (IEEE, 2022), pp. 1–6

M. Zeng, N.P. Nguyen, O.A. Dobre, H.V. Poor, Securing downlink massive MIMO-NOMA networks with artificial noise. IEEE J. Sel. Top. Signal Process. 13 (3), 685–699 (2019)

M. Zeng, A. Yadav, O.A. Dobre, G.I. Tsiropoulos, H.V. Poor, Capacity comparison between MIMO-NOMA and MIMO-OMA with multiple users in a cluster. IEEE J. Sel. Areas Commun. 35 (10), 2413–2424 (2017)

Q. Zhang, Q. Li, J. Qin, Robust beamforming for nonorthogonal multiple-access systems in miso channels. IEEE Trans. Veh. Technol. 65 (12), 10231–10236 (2016)

S. Zhang, Z. Yang, M. Chen, D. Liu, K.K. Wong, H.V. Poor, Beamforming design for the performance optimization of intelligent reflecting surface assisted multicast MIMO networks. arXiv preprint arXiv:2208.07048 (2022)

Y. Zhang, K. Peng, X. Wang, J. Song, Performance analysis and code optimization of IDMA with 5G new radio LDPC code. IEEE Commun. Lett. 22 (8), 1552–1555 (2018)

Z. Zhang, J. Liu, Y. Li, Design and analysis of a multi-input multi-output system for high power based on improved magnetic coupling structure. Energies 15 (5), 1684 (2022)

Z. Zhang, H. Sun, R.Q. Hu, Downlink and uplink non-orthogonal multiple access in a dense wireless network. IEEE J. Sel. Areas Commun. 35 (12), 2771–2784 (2017)

J. Zhao, Y. Liu, K.K. Chai, A. Nallanathan, Y. Chen, Z. Han, Spectrum allocation and power control for non-orthogonal multiple access in HetNets. IEEE Trans. Wirel. Commun. 16 (9), 5825–5837 (2017)

Z. Zhao, W. Chen, An adaptive switching method for sum rate maximization in downlink MISO-NOMA systems, in GLOBECOM 2017-2017 IEEE Global Communications Conference (IEEE, 2017), pp. 1–6

B. Zheng, Q. Wu, R. Zhang, Intelligent reflecting surface-assisted multiple access with user pairing: NOMA or OMA? IEEE Commun. Lett. 24 (4), 753–757 (2020)

X. Zou, B. He, H. Jafarkhani, An analysis of two-user uplink asynchronous non-orthogonal multiple access systems. IEEE Trans. Wirel. Commun. 18 (2), 1404–1418 (2019)

Download references

Acknowledgements

The Deanship of Scientific Research (DSR) at King Abdulaziz University (KAU), Jeddah, Saudi Arabia, has funded this project, under Grant No. (KEP-MSc: 63-135-1443).

The Deanship of Scientific Research (DSR) at King Abdulaziz University (KAU), Jeddah, Saudi Arabia, has funded this project, under grant no. (KEP-MSc: 63-135-1443).

Author information

Authors and affiliations.

Faculty of Engineering Sciences and Technology, Hamdard University, Sharae Madinat Al-Hikmah, Karachi, 74600, Pakistan

Sadiq Ur Rehman & Anwaar Manzar

Faculty of Engineering and Technology, Usman Institute of Technology University (UITU), ST-13, Block-7, Gushan-e-Iqbal, Abu-Hasan Isphahani Road, Karachi, 75300, Pakistan

Jawwad Ahmad

Electrical and Computer Engineering Department, King Abdulaziz University, 21589, Jeddah, Saudi Arabia

Muhammad Moinuddin

Center of Excellence in Intelligent Engineering Systems (CEIES), King Abdulaziz University, 21589, Jeddah, Saudi Arabia

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Jawwad Ahmad .

Ethics declarations

Conflict of interest.

Authors do not have any conflict of interest for the submitted work.

Code Availability

Code can be made available on request.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Rehman, S.U., Ahmad, J., Manzar, A. et al. Beamforming Techniques for MIMO-NOMA for 5G and Beyond 5G: Research Gaps and Future Directions. Circuits Syst Signal Process 43 , 1518–1548 (2024). https://doi.org/10.1007/s00034-023-02517-w

Download citation

Received : 05 March 2023

Revised : 12 September 2023

Accepted : 13 September 2023

Published : 02 November 2023

Issue Date : March 2024

DOI : https://doi.org/10.1007/s00034-023-02517-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Beamforming
• Heterogeneity
• Channel statistics
• Outage Probability
• Find a journal
• Publish with us
• Track your research