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Research areas in 5G Technology

We are currently on the cusp of 5G rollout. As industry experts predict , 5G deployments will gain momentum, and the accessibility of 5G devices will grow in 2020 and beyond. But as the general public waits for mass-market 5G devices, our understanding of this new technology is continuing to develop. Public and private organizations are exploring several research areas in 5G technology, helping to create more awareness of breakthroughs in this technology, its potential applications and implications, and the challenges surrounding it. 

What is especially clear at this point is that 5G technology offers a transformative experience for mobile communications around the globe. Its benefits, which include higher data rates, faster connectivity, and potentially lower power consumption, promise to benefit industry, professional users, casual consumers, and everyone in between. As this article highlights, researchers have not yet solved or surmounted all of the challenges and obstacles surrounding the wide scale deployment of 5G technology. But the potential impact that it will have on the entire matrix of how we communicate is limited only by the imagination of the experts currently at its frontier. 

New developments and applications in 5G technologies

Much of the transformative impact of 5G stems from the higher data transmission speeds and lower latency that this fifth generation of cellular technology enables. Currently, when you click on a link or start streaming a video, the lag time between your request to the network and its delivery to your device is about twenty milliseconds. 

That may not seem like a long time. But for the expert mobile robotics surgeon, that lag might be the difference between a successful or failed procedure. With 5G, latency can be as low as one millisecond. 

5G will greatly increase bandwidth capacity and transmission speeds. Wireless carriers like Verizon and AT&T have recorded speeds of one gigabyte per second. That’s anywhere from ten to one hundred times faster than an average cellular connection and even faster than a fiber-optic cable connection. Such speeds offer exciting possibilities for new developments and applications in numerous industries and economic sectors.

E-health services

For example, 5G speeds allow telemedicine services to enhance their doctor-patient relationships by decreasing troublesome lag times in calls. This helps patients return to the experience of intimacy they are used to from in-person meetings with health-care professionals. 

As 5G technology continues to advance its deployment, telemedicine specialists find that they can live anywhere in the world, be licensed in numerous states, and have faster access to cloud data storage and retrieval. This is especially important during the COVID-19 pandemic , which is spurring new developments in telemedicine as a delivery platform for medical services. 

Energy infrastructure

In addition to transforming e-health services, the speed and reliability of 5G network connectivity can improve the infrastructure of America’s energy sector with smart power grids. Such grids bring automation to the legacy power arrangement, optimizing the storage and delivery of energy. With smart power grids, the energy sector can more effectively manage power consumption and distribution based on need and integrate off-grid energy sources such as windmills and solar panels.

Another specific area to see increased advancement due to 5G technology is artificial intelligence (AI). One of the main barriers to successful integration of AI is processing speeds. With 5G, data transfer speeds are ten times faster than those possible with 4G. This makes it possible to receive and analyze information much more efficiently. And it puts AI on a faster track in numerous industries in both urban and rural settings. 

In rural settings, for example, 5G is helping improve cattle farming efficiency . By placing sensors on cows, farmers capture data that AI and machine learning can process to predict when cows are likely to give birth. This helps both farmers and veterinarians better predict and prepare for cow pregnancies.

However, it’s heavily populated cities across the country that are likely to witness the most change as mobile networks create access to heretofore unexperienced connectivity. 

Smart cities

Increased connectivity is key to the emergence of smart cities . These cities conceive of improving the living standards of residents by increasing the connectivity infrastructure of the city. This affects numerous aspects of city life, from traffic management and safety and security to governance, education, and more. 

Smart cities become “smarter” when services and applications become remotely accessible. Hence, innovative smartphone applications are key to smart city infrastructure. But the potential of these applications is seriously limited in cities with spotty connectivity and wide variations in data transmission speed. This is why 5G technology is crucial to continued developments in smart cities.

Other applications

Many other industries and economic sectors will benefit from 5G. Additional examples include automotive communication, smart retail and manufacturing. 

Wave spectrum challenges with 5G

While the potential applications of 5G technology are exciting, realizing the technology’s potential is not without its challenges. Notably, 5G global upgrades and changes are producing wave spectrum challenges.

A number of companies, such as Samsung, Huawei Technologies, ZTE Corporation, Nokia Networks, Qualcomm, Verizon, AT&T, and Cisco Systems are competing to make 5G technology available across the globe. But while in competition with each other, they all share the same goal and face the same dilemma.

Common goal

The goal for 5G is to provide the requisite bandwidth to every user with a device capable of higher data rates. Networks can provide this bandwidth by using a frequency spectrum above six gigahertz . 

Though the military has already been using frequencies above six gigahertz, commercial consumer-based networks are now doing so for the first time. All over the globe, researchers are exploring the new possibilities of spectrum and frequency channels for 5G communications. And they are focusing on the frequency range between twenty-five and eighty-six gigahertz.

Common dilemma

While researchers see great potential with a high-frequency version of 5G, it comes with a key challenge. It is very short range. Objects such as trees and buildings cause significant signal obstruction, necessitating numerous cell towers to avoid signal path loss. 

However, multiple-input, multiple-output (MIMO) technology is proving to be an effective technique for expanding the capacity of 5G connectivity and addressing signal path challenges. Researchers are keying into MIMO deployment due to its design simplicity and multiple offered features. 

A massive MIMO network can provide service to an increased multiplicity of mobile devices in a condensed area at a single frequency simultaneously. And by facilitating a greater number of antennas, a massive MIMO network is more resistant to signal interference and jamming.

Even with MIMO technology, however, line of sight will still be important for high-frequency 5G. Base stations on top of most buildings are likely to remain a necessity. As such, a complete 5G rollout is potentially still years away. 

Current solutions and the way forward

In the interim, telecommunication providers have come up with an alternative to high-frequency 5G— “midband spectrum.” This is what T-Mobile uses. But this compromise does not offer significant performance benefits in comparison to 4G and thus is unlikely to satisfy user expectations. 

Despite the frequency challenges currently surrounding 5G, it is important to keep in mind that there is a common evolution with new technological developments. Initial efforts to develop new technology are often complex and proprietary at the outset. But over time, innovation and advancements provide a clear, unified pathway forward.

This is the path that 5G is bound to follow. Currently, however, MIMO technological advancements notwithstanding, 5G rollout is still in its early, complex phase.

Battery life and energy storage for 5G equipment

For users to enjoy the full potential of 5G technology, longer battery life and better energy storage is essential. So this is what the industry is aiming for.

Currently, researchers are looking to lithium battery technology to boost battery life and optimize 5G equipment for user expectations. However, the verdict is mixed when it comes to the utility of lithium batteries in a 5G world. 

Questions about battery demands and performance

In theory, 5G smartphones will be less taxed than current smartphones. This is because a 5G network with local 5G base stations will dramatically increase computation speeds and enable the transfer of the bulk of computation from your smartphone to the cloud. This means less battery usage for daily tasks and longer life for your battery. Or does it?

A competing theory focuses on the 5G phones themselves. Unlike 4G chips, the chips that power 5G phones are incredibly draining to lithium batteries. 

Early experiments indicate that the state-of-the-art radio frequency switches running in smartphones are continually jumping from 3G to 4G to Wi-Fi. As a smartphone stays connected to these different sources, its battery drains faster.

The present limited infrastructure of 5G exacerbates this problem. Current 5G smartphones need to maintain a connection to multiple networks in order to ensure consistent phone call, text message, and data delivery. And this multiplicity of connections contributes to battery drain.

Until the technology improves and becomes more widely available, consumers are left with a choice: the regular draining expectations that come with 4G devices or access to the speeds and convenience of 5G Internet. 

Possibilities for improvement on the horizon

Fortunately, what can be expected with continuous 5G rollout is continuous improvements in battery performance. As 5G continues to expand across the globe, increasing the energy density and extending the lifetime of batteries will be vital. So market competition for problem-solving battery solutions promises to be fierce and drive innovation to meet user expectations. 

Additional research areas in 5G technology

While research in battery technology remains important, researchers are also focusing their attention on a number of other areas of concern. This research is likewise aimed at meeting user expectations and realizing the full potential of 5G technology as it gains more footing in public and private sectors. 

Small cell research

For example, researchers are focusing on small cells to meet the much higher data capacity demands of 5G networks. As mobile carriers look to densify their networks, small cell research is leading the way toward a solution.

Small cells are low-powered radio access points that take the place of traditional wireless transmission systems or base stations. By making use of low-power and short-range transmissions in small geographic areas, small cells are particularly well suited for the rollout of high-frequency 5G. As such, small cells are likely to appear by the hundreds of thousands across the United States as cellular companies work to improve mobile communication for their subscribers. The faster small cell technology advances, the sooner consumers will have specific 5G devices connected to 5G-only Internet. 

Security-oriented research

Security is also quickly becoming a major area of focus amid the push for a global 5G rollout. Earlier iterations of cellular technology were based primarily on hardware. When voice and text were routed to separate physical devices, each device managed its own network security. There was network security for voice calls, network security for short message system (SMS), and so forth.

5G moves away from this by making everything more software based. In theory, this makes things less secure, as there are now more ways to attack the network. Originally, 5G did have some security layers built in at the federal level. Under the Obama administration, legislation mandating clearly defined security at the network stage passed. However, the Trump administration is looking to replace these security layers with its own “national spectrum strategy.”

With uncertainty about existing safeguards, the cybersecurity protections available to citizens and governments amid 5G rollout is a matter of critical importance. This is creating a market for new cybersecurity research and solutions—solutions that will be key to safely and securely realizing the true value of 5G wireless technology going forward.

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5G, 6G, and Beyond: Recent advances and future challenges

• Published: 20 January 2023
• Volume 78 , pages 525–549, ( 2023 )

Cite this article

• Fatima Salahdine   ORCID: orcid.org/0000-0003-4330-906X 1 ,
• Tao Han 2 &
• Ning Zhang 3  

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With the high demand for advanced services and the increase in the number of connected devices, current wireless communication systems are required to expand to meet the users’ needs in terms of quality of service, throughput, latency, connectivity, and security. 5G, 6G, and Beyond (xG) aim at bringing new radical changes to shake the wireless communication networks where everything will be fully connected fulfilling the requirements of ubiquitous connectivity over the wireless networks. This rapid revolution will transform the world of communication with more intelligent and sophisticated services and devices leading to new technologies operating over very high frequencies and broader bands. To achieve the objectives of the xG networks, several key technology enablers need to be performed, including massive MIMO, software-defined networking, network function virtualization, vehicular to everything, mobile edge computing, network slicing, terahertz, visible light communication, virtualization of the network infrastructure, and intelligent communication environment. In this paper, we investigated the recent advancements in the 5G/6G and Beyond systems. We highlighted and analyzed their different key technology enablers and use cases. We also discussed potential issues and future challenges facing the new wireless networks.

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Salahdine, F., Han, T. & Zhang, N. 5G, 6G, and Beyond: Recent advances and future challenges. Ann. Telecommun. 78 , 525–549 (2023). https://doi.org/10.1007/s12243-022-00938-3

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• Published: 12 January 2021

5G as a wireless power grid

• Aline Eid 1 ,
• Jimmy G. D. Hester 1 , 2 &
• Manos M. Tentzeris 1  

Scientific Reports volume  11 , Article number:  636 ( 2021 ) Cite this article

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5G has been designed for blazing fast and low-latency communications. To do so, mm-wave frequencies were adopted and allowed unprecedently high radiated power densities by the FCC. Unknowingly, the architects of 5G have, thereby, created a wireless power grid capable of powering devices at ranges far exceeding the capabilities of any existing technologies. However, this potential could only be realized if a fundamental trade-off in wireless energy harvesting could be circumvented. Here, we propose a solution that breaks the usual paradigm, imprisoned in the trade-off between rectenna angular coverage and turn-on sensitivity. The concept relies on the implementation of a Rotman lens between the antennas and the rectifiers. The printed, flexible mm-wave lens allows robust and bending-resilient operation over more than 20 GHz of gain and angular bandwidths. Antenna sub-arrays, rectifiers and DC combiners are then added to the structure to demonstrate its combination of large angular coverage and turn-on sensitivity—in both planar and bent conditions—and a harvesting ability up to a distance of 2.83 m in its current configuration and exceeding 180 m using state-of-the-art rectifiers enabling the harvesting of several μW of DC power (around 6 μW at 180 m with 75 dBm EIRP).

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Introduction.

Our era is witnessing a rapid development in the field of millimeter-wave (mm-wave) and Internet of Things (IoT) technologies with a projected 40 billion IoT devices to be installed by 2025 1 . This could result in a huge number of batteries needing to be continuously charged and replaced. The design and realization of energy-autonomous, self-powered systems: the perpetual IoT, is therefore highly desirable. One potential way of satisfying these goals is through electromagnetic energy harvesting. A powerful source for electromagnetic scavenging is mm-wave energy, present in the fifth-generation (5G) of mobile communications bands (above 24 GHz), where the limits of allowable transmitted Effective Isotropic Radiated Power (EIRP) by the Federal Communications Commission (FCC) regulations are pushed beyond (reaches 75 dBm) that of their lower-frequency counterparts. Following the path loss model defined by the 3rd Generation Partnership Project Technical Report 3GPP TR 38.901 (release 16) in outdoor Urban Macro Line of Sight conditions (UMa LOS), the power density expected to be received at 28 GHz for a transmitted power of 75 dBm EIRP is 28 μW cm −2 at a distance of 100 m away from the transmitter. This demonstrates the ability of 5G to create a usable network of wireless power. In addition to the advantage of high transmitted power available at 5G, moving to mm-wave bands allows the realization of modular antennas arrays instead of single elements, thereby allowing a fine scaling of their antenna aperture, which can more than compensate for the high path loss at these frequencies through the addition of extremely-large gains 2 . However, one limitation accompanies large gain antennas: their inability to provide a large angular coverage. As the relative orientations of the sources and harvesters are generally unknown, the use of large aperture mm-wave harvesters may seem limiting and impossible. Individual rectennas, constituted of small antenna elements, can realistically be DC combined. However, this approach does not increase the turn-on sensitivity (lowest turn-on power) of the overall rectenna system: RF combination is needed.

Beamforming networks (BFNs) are used to effectively create simultaneous beam angular coverage with large-gain arrays, by mapping a set of directions to a set of feeding ports. An important class of these multiple networks is the microwave passive BFN that has been widely used in switched-beam antenna systems and applications. Hybrid combination techniques, based on Butler matrix networks, have been used in previous works for energy harvesting at lower frequencies 3 , 4 ,—more specifically at 2.45 GHz—to achieve wider angular coverage harvesting. However, these Ultra-High Frequency (UHF) arrays are impractically large for IoT applications and the implementation of their Butler matrices at higher frequencies would necessitate costly high-resolution fabrication. While sub-optimal—because of its large size—in the UHF band, the Rotman lens becomes the BFN of choice in the realm of mm-wave energy harvesting. Compared to their lower frequencies counterpart, fewer implementations are presented in the literature targeting energy harvesting at higher frequencies, more specifically 24 GHz and above. However, these systems later displayed in the table of comparison 5 , 6 , 7 , suffer from a narrow angular coverage.

In this paper, the authors demonstrate a full implementation of an entirely flexible, bending-resilient and simultaneously high gain and large angular coverage system for 5G/mm-wave energy harvesting based on a Rotman lens. For IoT applications, there is a benefit to making extremely low-profile devices that can conformally fit onto any surface in the environment such as walls, bodies, vehicles, etc. Therefore, thanks to the use of mm-waves, antennas with such features can be readily designed and fabricated. A Rotman lens-based rectenna has been first proposed in 8 , where a preliminary prototype and approach were presented, resulting in a quasi-flexible system, 80° angular coverage and 21-fold increase in the harvested power compared to a non-Rotman-based system. Here, the previously-predicted potential of 5G-powered nodes for the IoT and long-range passive mm-wave Radio Frequency IDentification (RFID) devices, is further taken advantage of, and effectively demonstrated. In order to do so, a thorough analysis of the lens itself—a structure that was not revealed in 8 —is first presented, exposing its key design parameters and resulting measured broadband behavior tested in both planar and bent conditions over more than 20 GHz of bandwidth. In addition, a scalability study of the approach, outlining the optimal size of such a system is reported, thereby proving the extent of the capability of providing a combination of good array factor and wide beam coverage. The novelty of this system also lies in the realization of a fully-flexible 28 GHz Rotman-lens-based rectenna system, completed by the design of a new DC combiner on a flexible 125 μm-thin polyimide Kapton substrate. The new DC combiner uses a reduced number of bypass diodes and increases the angular coverage of the system by more than 30% compared to 8 . Furthermore, the frequency-broadband behavior enabled by the use of the Rotman lens makes the full rectenna system bending-resilient, a property now demonstrated through its characterizations in flexing and conformally-mounted configurations. Finally, the system’s potential for long-range mm-wave harvesting is expressed for the first time, by reporting an unprecedented harvesting range of 2.83 m.

Experiments, results and discussions

Rotman lens scalability study for harvesting applications.

The Rotman lens, introduced in the 1960s, constitutes one of the most common and cost-effective designs for BFNs and is commonly utilized to enable multibeam phased array system 9 and wide-band operation, thanks to its implementation of true-time-delays 10 . By properly tuning the shape of the lens according to the geometrical optics approximation with the goal of focalizing plane waves impinging on the antenna side of the lens to different focal points on the beam-ports side of the lens, one achieves a lens-shaped structure with two angles of curvatures: one on the beam-ports side, and the other on the antenna side 11 . Because the lens is capable of focusing the energy coming from a given direction into its geometrically-associated beam port, the proposed scheme loads each of these ports with a rectifier, thereby channeling the energy coming from any direction to one of the rectifiers as shown in Fig. 1 a. This subsection investigates the effect of varying the number of antenna ports Na and beam ports Nb in the Rotman lens on its maximum array factor and angular coverage. The ( Na , Nb ) set, resulting in the best combination, will define the Rotman lens design parameters used for this work. Structures of varying sizes were designed using Antenna Magus and identical material parameters (substrate, conductors) as the ones of the presented design, before being simulated in CST STUDIO SUITE 2018. The simulated data was then processed in MATLAB to output the array factors created by the respective lens structures using a modified version of Eq. ( 1 ) 12 , presented next in Eq. ( 2 ):

where AF , n , Na , k , d , \(\theta\) and \(\beta\) are, respectively, the lossless array factor, the antenna number, the total number of antenna ports, the wave vector, the spacing between the elements, the direction of radiation and the difference in phase excitation between the elements. Since this formula describes a lossless array with a single antenna port, we introduced the following equation that takes into account the losses induced by the feeding network as well as the introduction of multiple feeding ports.

where \(AF_j\) and \(S_{nj}\) are, respectively, the array factor for beam port j and the S parameters between antenna ports n and beam ports j . The maximum value of the array factors as well as their total (accounting for the aggregated coverage of all ports) 3 dB beamwidths where then tabulated. The five simulated lenses had the following ( Na , Nb ) combinations: (4,3), (8,6) representing the system implemented in this work, (16,12), (32,24) and (64,48). Figure 1 b shows the increase in the array factor until reaching a peak of around 7.8 dB for a lens surrounded by 16 antennas and 12 beam ports, after which the array factor starts dropping, down to approximately 5.2 dB for a 64 antennas structure with 48 beam ports. The array factor reduction is explained by the increased losses within the lens accompanied by the increase of complexity and internal reflections, as the lens grows in electrical size. The same plot shows the decrease in angular coverage from 180° with 4 antennas down to 80° with 64 antennas. This study shows that the combination composed of eight antennas and six beam ports, offers a nearly optimal compromise, with these materials, between a high array factor of 5.95 dB and a 120° total angular coverage, while maintaining a reasonable number of antennas and beam ports. It should be noted that the choice of the number of beam ports is related to the 3dB-beamwidth of the individual antennas, the reason for which will be detailed later.

( a ) Dual combining (RF + DC) enabled by the use of the Rotman lens between the antennas and the rectifiers, ( b ) plot of the simulated maximum array factors and angular coverages for different-size Rotman lenses and ( c ) picture of the fabricated Rotman lens structure.

Flexible broadband Rotman lens design

After setting the number of antenna ports and beam ports, the design was printed on flexible copper-clad Liquid Crystal Polymer (LCP) substrate ( \(\varepsilon _r = 3.02\) and \(\hbox {h}= 180\,\upmu \hbox {m}\) ) using an inkjet-printed masking technique followed by etching, resulting in the structure shown in Fig. 1 c. It should be noted that the use of impedance-matched dummy ports is common with Rotman lenses 13 , 14 , 15 , 16 . Nevertheless, the goal in the implementation hereby described is not (as is usually the case) the generation of clean beam patterns with low side-lobe levels. Here, the lens’ properties are used for harvesting. Consequently, as long as the presence of the side lobes does not significantly interfere with the level of the array factor at broadside, side lobes are of no concern. Such a structure, including eight antenna ports and six beam ports—and, therefore, six radiating directions—was designed, simulated, and tuned. The structure, shown in Fig. 1 c, with the antenna ports connected to matched loads, was then tested in planar and bent configurations—cylinders with different bending radii ranging from 1.5 to 2.5 in. radii—to assess the effect of bending on the S parameters behavior. Figure 2 a shows the measured reflection coefficient of the Rotman lens at beam port 4 for four different scenarios, in comparison with the simulated structure in a planar position. The results reveal the Rotman lens’ ability to be mounted on curved surfaces down to a radius R = 1.5″, while maintaining a stable matching and minuscule losses compared to being held in a planar position.

( a ) Plot of the simulated and measured reflection coefficients at beam port 4 under planar and bent conditions and ( b ) Plots of the maximum array factors and angular directions of beam ports P1, P3 and P5 with respect to frequency.

The gain and angular bandwidths of this structure—defined by the frequency range in which the maximum array factor and angular direction per beam are stable within 3 dB and 5° respectively,—are studied next. The ultimate assessment of these properties involves calculating the beams’ magnitude and angular directions over a wide range of frequencies 17 , in order to ascertain their stability or lack thereof. For this purpose, the maximum array factors were calculated and the beams’ angular directions were extracted and plotted in Fig.  2 b for the first, third and fifth beam ports, P1, P3 and P5, representing the edge, secondary and central beams in this symmetrical structure. These plots prove the unique capabilities offered by the Rotman lens; although the Rotman lens is designed at a specific frequency—28 GHz in this work—this analysis proves that both the magnitude and the angular direction of the beams remain relatively stable over a very wide frequency range. In Fig. 2 b, three plots refer to the maximum array factors of the three beam ports, where minor fluctuations between 4 and 7 dB are observed over the range from 10 to 43 GHz for ports P3 and P5 and similar fluctuations over a fairly reduced frequency range for the extreme edge beam P1. On the same graph, three plots present the angular direction’s stability of P1, P3 and P5 beams, where P3 (in particular) preserves its angular direction over 33 GHz of bandwidth. The lens’ angular coverage resides between ports 1 and 6 and can be extracted from Fig. 2 b. Knowing that the structure is symmetrical and that beam port P1 is at around \({-54}^\circ\) , the overall structure covers an angle larger than 100° in front of the lens, a result further detailed in the next subsection. It should be noted that such a beamwidth is maintained over a large angular bandwidth exceeding 20 GHz, as shown in Fig. 2 b. This study demonstrates the stability and robustness of a low-cost, printed and flexible mm-wave Rotman lens structure, tested with respect to bending and frequency, and supports the choice of such an architecture at the heart of the harvesting system proposed in this work.

Flexible, high-gain and wide-angular-coverage mm-wave Rotman-lens-based antenna array

Eight of the linear antenna sub-arrays introduced in 8 were then added to the antenna ports of the array, and its beam-ports were extended by microstrip lines to enable their connection to end-launch \({2.92}\,\upmu \hbox {m}\) connectors. The antenna sub-array consists of five serially-fed patch antenna elements, providing an operation centered at 28.55 GHz with a reflection coefficient \(S_{11}\) lower than \({-20}\)  dB within this range. Their E-plane beamwidth of about \({18}^\circ\) (provided by the five antennas) is appropriate for most use cases, where environments expand mostly horizontally. Its simulations showed a gain of 13 dBi and a H-plane beamwidth of 80° in the plane perpendicular to the linear array. In this configuration, six beams were chosen to intersect at angles providing 3dB lower gain than broadside. Eight antennas provide a 3dB-beamwidth of 15°, which covers a total of \(6\times {18}^\circ = {108}^\circ\) in front of the array. The design was then also printed on flexible LCP substrate, resulting in the structure shown in Fig. 3 a, mounted on a 1.5″ radius cylinder. The radiation properties of the lens-based antenna system were simulated using the time-domain solver of CST STUDIO SUITE 2018, resulting in the six gain plots shown in Fig. 3 b. The gain of the Rotman lens at every port was also accurately measured using a 20 dBi transmitter horn antenna and by terminating all five remaining ports with a \({50}\,\Omega\) load for every port measurement to guarantee the proper operation of the lens. Both simulated and measured radiation patterns (shown in Fig. 3 b) display a remarkable similarity with a measured gain of approximately 17 dBi, and an angular coverage of around 110°, thereby validating the operation of the antenna array. The gains on the first three ports were also measured for the bent structure over a curvature of 1.5″ radius, shown in Fig. 3 a and compared to the measured results on a planar surface. The previous subsection in addition to previous works 18 , 19 have demonstrated that the performance of the Rotman lens is not deteriorated by wrapping or folding the structure compared to its conventional planar counterpart. However, after adding the antenna arrays, bending the structure can indeed have effects on its phase response, especially if the structure is large and the bending is severe. Figure  3 c shows the gains of P1, P2 and P3 for the two scenarios (three ports only because the structure is symmetrical), demonstrating again the ability of the lens in maintaining a stable gain (especially over the center beams) upon bending. The beam located at the edge, however, suffers additional deterioration in received power under bending, because of the shift of the source away from the broadside of the bent antenna arrays.

( a ) Picture of the flexible Rotman-lens-based antenna array, ( b ) measured (solid lines) and simulated (dashed lines) gains of the antenna array held in a planar position and ( c ) measured gains of the antenna array for beams P1, P2 and P3 only (because of the symmetry of the structure) in planar and bent conditions.

Fully-flexible 28 GHz Rotman lens-based system

Rotman-lens-based rectenna.

In this section, the fully-flexible rectenna system—based on the Rotman lens and a new DC combiner network—is presented. This architecture, shown in Fig. 4 a, consists of a series of eight antenna sub-arrays attached to the Rotman lens from one side, facing six rectifiers at the opposite side where DC serial combination is implemented. The basic rectenna elements, that are the antenna and the rectifier, are presented in details in 8 . The diode used in this work is the MA4E2038 Schottky barrier diode from Macom. The Rotman-based rectenna was first characterized as a function of its received power density. The system was positioned at a specific harvesting angle (approximately \(-25^\circ\) ) and illuminated with a horn antenna with a gain of 20 dBi, placed at a distance of 52 cm away from the rectenna array, within the far field region starting at 23 cm, and outputting powers ranging from 18 to 25 dBm, corresponding to an RF input power sweep from around − 9 dBm to − 2 dBm. The array was loaded with its optimal load impedance of 1  \(\hbox {k}\Omega\) , corresponding to the optimal load of a single rectifier—since only one rectifier will be “ON” at a time, given that the Rotman lens focalizes all the power to one beam port depending on the direction of the incoming wave—as detailed earlier. The results of this experiment are shown in Fig. 4 b, where the harvested voltages and powers of the array are shown. It can be observed that, at low powers, the Rotman-based rectenna effortlessly produces an output. The Rotman-based rectenna turns on well below − 6 dBm cm −2 , which compares quite favorably to the literature 6 . The output voltage of the rectenna was also measured over its operating frequency range. Like in the first experiment, the system was positioned at the same harvesting angle, at a range of 25 cm away from the source’s horn antenna. The output voltages under open load conditions were recorded and plotted, as shown in Fig. 4 c for the Rotman lens-based rectenna, for \(P_d = {9}\,{\hbox{dBm cm}}^{-2}\) , \(P_d = {10.5}\,{\hbox{dBm cm}}^{-2}\) and \(P_d = {12}\,{\hbox{dBm cm}}^{-2}\) incident power densities. The plots present a wide frequency coverage—from 27.8 to 29.6 GHz.

( a ) Picture of the fully-flexible Rotman-based rectenna, ( b ) plot of the measured voltages and output powers versus incident power density for the Rotman-based rectenna and ( c ) plot of the measured voltages with respect to frequency for the Rotman-based rectenna.

Flexible DC combining network

Power summation is very critical when it comes to the unbalanced rectification outputs produced from realistic RF sources, and can be implemented differently depending on its costs and benefits 20 .

This paper does not rely on a direct voltage summation topology (i.e. back-to-back RF diodes); however, it introduces a minimalist architecture relying on a total of \(2\times (N-1)\) bypass diodes, where N is the number of RF or rectifying diodes. Equipped with a low turn-on voltage of 0.1 V, the Toshiba 1SS384TE85LF bypass diodes used in the DC combiner design create a low resistance current path around all other rectifiers that received very low or close to zero RF power. This topology is optimal when only one diode is turned on, which can be assumed if a single, dominant source of power irradiates this particular design from a given direction. This new combiner circuit is shown in the schematic of Fig. 5 a. This simplified schematic—shown for four rectifying diodes—uses different colors to highlight the paths that the current will take for every case where an RF diode turning “ON” while the serially-connected diodes are “OFF”. This DC combiner was then fabricated on a flexible \({125\,\upmu \mathrm{m}}\) -thin polyimide Kapton substrate and connected to the Rotman lens-based rectenna through a series of single connectors to make the entire system fully flexible and bendable. The harvested power under a load of 1  \(\hbox {k}\, \Omega\) versus the angle of incidence of the mm-wave energy source for the Rotman-lens-based rectenna is compared for both rigid (presented in 8 , and relying on \(2\times N\) bypass diodes) and flexible new DC combiners. For this experiment, a horn transmitter antenna was used to send 25 dBm of RF power at 28.5 GHz to the lens placed 70 cm away, as shown in Fig. 5 b, while the array was precisely rotated in angular increments of 5°. Figure 6 a shows that the new DC combiner, with a reduced number of diodes, was able to provide a complete angular coverage of almost 110° over the entire lens spectrum as presented in Fig. 3 b, thus solving the voltage nulling occurring at the first and last ports, using the rigid DC combiner adopted previously in 8 . The new DC combiner offers therefore, an increase of more than 30% in the system’s spatial angular in addition to enabling a fully-bendable structure due to the unique fabrication on flexible Kapton substrate and connection to the rectenna using individual interconnects.

( a ) Rotman-based rectenna power summation network and ( b ) picture of the setup used to measure the angular response of the rectenna.

( a ) Plot of the measured harvested powers by the rectenna with respect to the source’s incidence angle for the two DC combiners, rigid and flexible and ( b ) plots of the measured harvested powers and voltages with respect to the incident power density under different load conditions for the Rotman lens rectenna with and without the flexible DC combiner.

As mentioned earlier, the DC combiner is mainly used with the Rotman-lens-based rectenna to automatically direct the active rectifier’s output to a single DC common port, independent of which port this might be. An alternative to the DC combiner in the Rotman lens-based system, would be to manually connect to the active port if the location of the source were known. To study the effect of the implemented DC combiner on the turn-on sensitivity of the system, the output voltage of the rectenna was measured for a specific source location with and without the combiner over a range of RF transmitted power and load variations; the direction was chosen such that the non-DC-combined rectifier would output its maximum power. Figure  6 b shows eight different plots where three of them represent the harvested power with a direct connection to the active rectifier for 1  \(\hbox {k}\Omega\) , 10  \(\hbox {k}\Omega\) and 100  \(\hbox {k}\Omega\) conditions. Plotted with the same colors are the other three, representing the harvested power with the addition of the DC combiner for the same load values. The last two plots display the measured voltages with and without the combiner under open load conditions. The rectenna was placed 61 cm away from the transmitter horn antenna and the power was swept from 10 to 25 dBm. The results show the performance superiority in all considered load conditions when the contact is made directly to the rectifier and not through the DC combiner. The lens-based system is able to achieve a turn-on power as low as \(-15\,{\hbox{dBm cm}}^{-2}\) in this case. This behavior is explained by the voltage drop introduced by the bypass diodes present in the combiner—that consistently decrease the expected output voltage by 0.1 to 0.2 V—when one or two diodes are, respectively, added to the current path. The variation of load values also shows that the rectenna can achieve better efficiencies at lower loads. More importantly, the reduction in the turn-on sensitivity—the minimum power density required output 10 mV—induced by the combiner is only of about 2 dB in loaded conditions, while the combiner enables an increase in the angular coverage of the rectenna system from about 18° to 110°. The remarkable angular and high-power turn-on sensitivity offered by the Rotman-lens-based rectenna are finally benchmarked using the following table for comparison with several state-of-the-art works, as presented in literature. In Table  1 , the striking performance of the proposed system is displayed, highlighted by its flexibility and ability of achieving an angular coverage as large as 110° at extremely high turn-on sensitivity, thereby allowing mm-wave long-range harvesting in ad-hoc and conformal-mounting implementations.

Rectenna system performance under bending

This section displays the operation of the Rotman-lens-based system under different bending scenarios. This and previous work 18 , 19 show that the lens is able to maintain an efficient electromagnetic energy distribution across the output ports under convex and concave flexing conditions. The lens-based rectenna was placed on cylinders with different curvatures, 70 cm away from the transmitter sending 25 dBm of power at 28.5 GHz, as shown on Fig. 7 a. The voltage was collected using a load of 1  \(\hbox {k}\Omega\) for the planar and three bent conditions and plotted in Fig.  7 b with respect to the source’s angle of incidence. The graph shows an unprecedented consistency and stability in the system’s scavenging and rectification abilities, knowing that several sub-systems are exposed to warping and the pressures of bending: the antenna sub-arrays, the Rotman lens and the rectifiers. Slight attenuation can be observed at the edges, but the system otherwise performs unimpeded by the bending. This remarkable property qualifies this system as a perfect candidate for use in wearables, smart phones and ubiquitous, conformal 5G energy harvesters for IoT nodes.

( a ) Picture of the flexible Rotman lens-based rectenna placed on a 1.5″ radius cylinder and ( b ) measured harvested powers versus incidence angles for different curvatures, ( c ) long-range harvesting testing setup.

Long-range harvesting

As described earlier, one of the main appeals of the proposed approach is its ability to use the high EIRPs allowed for 5G base-stations while guaranteeing an extended beam angular coverage, which is a necessary feature for ad-hoc ubiquitous harvesting implementations. In order to demonstrate the lens based-rectenna for longer-distance harvesting and detect that maximum range, a high-performance antenna system—comprised of a 19 dBi conical horn antenna and a 300 mm-diameter PTFE dielectric lens (for high directivity) providing an additional 10 dB of gain—was used as shown in Fig. 7 c. With a transmitted power of 25 dBm (and an associated EIRP of approximately 54 dBm), corresponding to an incident power density of approximately − 6 dBm cm −2 , the lens-based rectenna displayed an extended range of 2.83 m under open load conditions, with an output voltage around 10 mV, thereby demonstrating (to our knowledge) the longest-ranging rectenna demonstration at mm-wave frequencies. With a transmitter emitting the allowable 75 dBm EIRP, the theoretical maximum reading range of this rectenna could extend to 16 m. In addition, the use of advanced diodes—designed for applications within the 5G bands and enabling rectifiers’ sensitivities similar to that common at lower (UHF) frequencies—are showing a potential path towards achieving a turn-on sensitivity of the rectifiers as low as − 30 dBm 21 , 22 . If this were practically applied to the Rotman lens system presented in this work, the harvesting range could be extended beyond 180 m (where the received power density for a transmitted power of 75 dBm is \({7.8}\,\upmu \hbox {W cm}^{-2}\) ), which is only slightly smaller than the recommended cell size of 5G networks 23 . This observation enables the striking idea that future 5G networks could be used not only for tremendously-rapid communications, but also as a ubiquitous wireless power grid for IoT devices.

Through the use of the Rotman lens, this paper demonstrates that the usual paradigm constrained by the (often considered fundamental) trade-off between the angular coverage and the turn-on sensitivity of a wireless harvesting system can be broken. Using the reported architecture, one can design and fabricate flexible mm-wave harvesters that can cover wide areas of space while being electrically large and benefit from the associated improvements in link budget (from source to harvester) and, more importantly, turn-on sensitivity. The approach has been shown, however, to only be scalable up to the degree where the additional incremental losses introduced by the growing lens counterbalance the increase in the aperture of the rectenna. Nevertheless, this inflection point only appears (in the particular context considered in this paper) after the arraying of 16 elements, or up to a scale of \(8\lambda\) . In the 5G Frequency Range 2 (FR2), this translates to harvesters of 4.5 cm to 9.6 cm in size, which are perfectly suited for wearable and ubiquitous IoT implementations. With the advent of 5G networks and their associated high allowed EIRPs and the availability of diodes with high turn-on sensitivities at 5G frequencies, several \({\upmu \hbox {W}}\) of DC power (around 6  \({\upmu \hbox {W}}\) with 75 dBm EIRP) can be harvested at 180 m. Such properties may trigger the emergence of 5G-powered nodes for the IoT and, combined with the long-range capabilities of mm-wave ultra-low-power backscatterers 24 , of long-range passive mm-wave RFIDs.

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Acknowledgements

This work was supported by the Air Force Research Laboratory and the NSF-EFRI. The work was performed in part at the Georgia Tech Institute for Electronics and Nanotechnology, a member of the National Nanotechnology Coordinated Infrastructure (NNCI), which is supported by the National Science Foundation (Grant ECCS-1542174).

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A.E. and J.H. conceived the idea, designed, and simulated the antenna arrays, rectifiers, Rotman lens, DC combiners and full rectennas. They also performed the measurements, interpreted results and wrote the paper. M.T. supervised the research and contributed to the general concept and interpretation of the results. All authors reviewed the manuscript.

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Eid, A., Hester, J.G.D. & Tentzeris, M.M. 5G as a wireless power grid. Sci Rep 11 , 636 (2021). https://doi.org/10.1038/s41598-020-79500-x

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Title: residual-based adaptive huber loss (rahl) -- design of an improved huber loss for cqi prediction in 5g networks.

Abstract: The Channel Quality Indicator (CQI) plays a pivotal role in 5G networks, optimizing infrastructure dynamically to ensure high Quality of Service (QoS). Recent research has focused on improving CQI estimation in 5G networks using machine learning. In this field, the selection of the proper loss function is critical for training an accurate model. Two commonly used loss functions are Mean Squared Error (MSE) and Mean Absolute Error (MAE). Roughly speaking, MSE put more weight on outliers, MAE on the majority. Here, we argue that the Huber loss function is more suitable for CQI prediction, since it combines the benefits of both MSE and MAE. To achieve this, the Huber loss transitions smoothly between MSE and MAE, controlled by a user-defined hyperparameter called delta. However, finding the right balance between sensitivity to small errors (MAE) and robustness to outliers (MSE) by manually choosing the optimal delta is challenging. To address this issue, we propose a novel loss function, named Residual-based Adaptive Huber Loss (RAHL). In RAHL, a learnable residual is added to the delta, enabling the model to adapt based on the distribution of errors in the data. Our approach effectively balances model robustness against outliers while preserving inlier data precision. The widely recognized Long Short-Term Memory (LSTM) model is employed in conjunction with RAHL, showcasing significantly improved results compared to the aforementioned loss functions. The obtained results affirm the superiority of RAHL, offering a promising avenue for enhanced CQI prediction in 5G networks.

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• Protecting Subscriber Identifiers with Subscription Concealed Identifier (SUCI) describes enabling SUCI protection, an optional capability new in 5G which provides important security and privacy protections for subscribers. 5G network operators are encouraged to enable SUCI on their 5G networks and subscriber SIMs and to configure SUCI to use a non-null encryption cipher scheme; this provides their customers with the advantages of SUCI’s protections.

You are invited to review the drafts and submit comments by September 16, 2024 . See the 5G Cybersecurity Project for more details.

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Virtual Coupling in Train using 5G; MODEL PREDICTION AND SIMULATION

5 Pages Posted: 21 Aug 2024

Aakanksha Jaiswar

Delhi technological university, aman bansal, anamika chauhan.

Date Written: August 14, 2024

This research explores the integration of 5G technology into Indian railway infrastructure through the implementation of Virtual Train Coupling (VC). Traditional train coupling systems are replaced with virtual connections facilitated by 5G networks, promising significant improvements in efficiency, safety, and operational flexibility. Virtual coupling eliminates the need for physical connections between trains, allowing them to operate closely without constraints. Leveraging the speed and low latency of 5G, trains communicate in real-time, adjusting speeds and distances based on surrounding conditions. This enhances safety by minimizing the risk of accidents and derailments. The study calculates the potential efficiency gains on Indian railway tracks with the adoption of VC. Additionally, it outlines the adaptable nature of trains once virtual connections are established, particularly beneficial at turnouts and stations where flexible operation is crucial. One of the key benefits of VC is its ability to dynamically form and dissolve train consists, reducing turnaround times and optimizing schedules. This contributes to increased operational efficiency and better alignment with cargo requirements for each journey. This research aims to implement 5G signaling to improve the efficiency of existing railway tracks in India, presenting a significant advancement in the nation's rail transportation infrastructure. By embracing 5G-enabled VC, Indian Railways can enhance safety, efficiency, and passenger experience, paving the way for a modernized and interconnected railway network.

Keywords: MPC AUMOATED VIRTUAL COUPLING

Suggested Citation: Suggested Citation

Delhi Technological University ( email )

Bawana Road Main Bawana Road, Delhi, Delhi 110042 India

Aman Bansal (Contact Author)

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Massive MIMO Systems for 5G and beyond Networks—Overview, Recent Trends, Challenges, and Future Research Direction

The global bandwidth shortage in the wireless communication sector has motivated the study and exploration of wireless access technology known as massive Multiple-Input Multiple-Output (MIMO). Massive MIMO is one of the key enabling technology for next-generation networks, which groups together antennas at both transmitter and the receiver to provide high spectral and energy efficiency using relatively simple processing. Obtaining a better understating of the massive MIMO system to overcome the fundamental issues of this technology is vital for the successful deployment of 5G—and beyond—networks to realize various applications of the intelligent sensing system. In this paper, we present a comprehensive overview of the key enabling technologies required for 5G and 6G networks, highlighting the massive MIMO systems. We discuss all the fundamental challenges related to pilot contamination, channel estimation, precoding, user scheduling, energy efficiency, and signal detection in a massive MIMO system and discuss some state-of-the-art mitigation techniques. We outline recent trends such as terahertz communication, ultra massive MIMO (UM-MIMO), visible light communication (VLC), machine learning, and deep learning for massive MIMO systems. Additionally, we discuss crucial open research issues that direct future research in massive MIMO systems for 5G and beyond networks.

1. Introduction

With globalization, present-day networks are facing high traffic demands, and to fulfill these needs, cellular systems are deployed within a few hundred-meter distances, and wireless Local Area Networks (LAN) are placed almost everywhere. Along with increased mobile broadband service, the introduction of new concepts like the Internet of Things (IoT) and Machine-to-Machine Communication (M2M) are also contributing to the increased wireless traffic. The global deployment of cellular service cultivates the cell phone users to be used to the mobile data in their day to day life tremendously. The services like video calling, online gaming, social media applications like Facebook, Twitter, WhatsApp, have changed our life drastically with the capabilities of the third-generation (3G), fourth-generation (4G), and fifth-generation (5G) networks, like lower latency and high data rate [ 1 ]. A full cell phone connected world is expected in the next few years, which will be mainly characterized by growth in users, connectivity, data traffic volume, and a wide range of applications. In the next few years, technology like augmented reality, virtual reality, ultra high definition video, 3D video, and features like a mobile cloud will become popular to enrich the ultimate user experience. From 2017–2022, smartphone traffic is expected to increase by ten times, and overall, mobile traffic will be increased by eight times [ 2 ]. Figure 1 shows the growth in mobile data traffic and the number of connected devices from 2017–2022 [ 3 ]. By the end of 2022, more than 90 percent of the traffic will come from cell phones. This colossal amount of mobile data traffic is challenging to manage with the capabilities of previous wireless generation systems.

Global mobile data traffic and growth in connected devices from 2017 to 2022.

The primary issue with the ongoing development of the wireless network is that it is dependent upon either increasing bandwidth (spectrum) or densifying the cells to achieve the required area throughput. These resources are rare and are reaching their saturation point within a few years. Also, increasing bandwidth or densifying the cells increases the cost of the hardware and increases latency. The third factor, which can improve area throughput, that is, spectral efficiency, has remained mostly untouched and unchanged during this rapid development and growth of the wireless network. An efficient wireless access technology that can increase the wireless area throughput without increasing the bandwidth or densifying the cell is essential to achieve the ongoing demands faced by the wireless carriers.

Massive Multiple-Input Multiple-Output (MIMO) is the most enthralling wireless access technology to deliver the needs of 5G and beyond networks. Massive MIMO is an extension of MIMO technology, which involves using hundreds and even thousands of antennas attached to a base station to improve spectral efficiency and throughput. This technology is about bringing together antennas, radios, and spectrum together to enable higher capacity and speed for the incoming 5G [ 4 , 5 ]. The capacity of massive MIMO to increase throughput and spectral efficiency has made it a crucial technology for emerging wireless standards [ 6 , 7 ]. The key here is the considerable array gain that massive MIMO achieves with a large number of antennas [ 8 ]. Massive MIMO is a key enabling technology for 5G and beyond networks, and as intelligent sensing system primarily rely on 5G and beyond networks to function, massive MIMO and intelligent sensing system are inextricably linked. The data collection from the large number of smart sensors using traditional multi-access schemes is very impractical as it leads to excessive latency, low data rate, and reduced reliability. Massive MIMO with huge multiplexing gain and beamforming capabilities can sense data from concurrent sensor transmission with much lower latency and provide sensors with higher data rates and reliable connectivity. Massive MIMO systems will perform a crucial role to allow information gathered through smart sensors to be transmitted in real-time to central monitoring locations for smart sensor applications such as an autonomous vehicle, remote healthcare, smart grids, smart antennas, smart highways, smart building, and smart environmental monitoring.

The rest of the paper is organized as follows: Section 2 provides details on the evolution of cellular networks from the first-generation (1G) to sixth-generation (6G) networks. Section 3 provides insights into key enabling technologies for 5G networks. The benefits of massive MIMO are explained in Section 4 , and Section 5 provides a brief description of the importance of massive MIMO for future generation networks. Section 6 reviews the challenges in massive MIMO systems and explains some state-of-the-art mitigation techniques. Section 7 discusses the possibility of our current phone to use the massive MIMO technology, and Section 8 presents the use of machine learning and deep learning in massive MIMO systems. Section 9 presents the active research topic on massive MIMO systems for future generation networks, and Section 10 concludes the paper summarizing the key ideas.

2. Evolution of Cellular Networks

The mobile communication era started in the early 1980s, and since then, mobile communication has experienced tremendous growth in the past few decades. Cellular networks have evolved from 1G to 5G and beyond. All cellular networks are composed of base stations, user equipment (phones), and core networks. The evolution from 1G to 6G is summarized in Figure 2 .

The evolution of mobile communication from 1G to 5G.

The 1G mobile networks were introduced in the early 1980s and used analog signals for voice-only services. 1G systems used Frequency Division Multiple Access (FDMA) and offered data rates up to 2.4 kbps. They had poor voice quality due to high interference. 1G systems included Advanced Mobile Phone Systems (AMPS), Total Access Communication System (TACS), and Nordic Communication System (NMTS) [ 4 ].

The second-generation (2G) mobile networks were introduced in the early 1990s and were generally considered digital versions of 1G networks. Along with voice services, they allowed Short Message Service (SMS) and basic email services. These systems used Code Division Multiple Access (CDMA) and Time Division Multiple Access (TDMA) and offered data rates from 14.4 kbps up to 64 kbps. 2G systems included Global System for Mobile Communication (GSM) and IS-95 CDMA. 2G networks have limited mobility and hardware capability [ 4 ].

2.3. 2.5G and 2.75G

2G technology was continuously improving to provide better data rates and services, and thus 2.5G networks were introduced with data rates up to 384 kbps. 2.5G systems included General Packet Radio Service (GPRS), Enhanced Data GSM Evolution (EDGE), and CDMA2000.

The 3G mobile networks were introduced in the early 2000s and were based on GSM and CDMA. These systems offered web browsing on mobile phones along with voice, Multimedia Message Support (MMS), and SMS services. 3G systems included Universal Mobile Telecommunication Systems (UMTS) and WCDMA. Smartphones became popular in the mid-2000s. 3G networks provided data rates upward of 384 Kbps, but they required large bandwidth and complex infrastructure.

Due to continuous demand for higher data rates, High-Speed Downlink Packet Access (HSDPA), High-Speed Uplink Packet Access (HSUPA), and High-Speed Packet Access (HSPA+) were introduced in 3G networks to increase data rates. These types of networks were referred to as 3.5G networks, and they provided data rates up to 2 Mbps. Although 3.5G provided a higher data rate, the implementation and the equipment was costly, and compatibility with 2G was very challenging [ 4 ].

The 4G mobile networks were introduced in the early 2010s. 4G networks offer data rates up to 100 Mbps and can handle more data traffic with a better quality of service (QoS). 4G networks include applications like video conferencing, online gaming, and mobile television. 4G systems include Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE), and LTE-Advanced (LTE-A), and it has feasible compatibility with older generation networks [ 9 ]. The frequency bands of 4G are considerably expensive, and high-end 4G enabled cell phones are required to operate 4G networks [ 9 ].

The 5G mobile networks are currently starting to be implemented and aim to be 100 times faster than current 4G networks. 5G networks will offer data rates up to 10 Gbps, low latency (in milliseconds), and greater reliability. Imagine that an HD movie can be downloaded in just a few seconds. This technology can support many Internet of Things (IoT) enabled devices and smart vehicles, as shown in Figure 3 . Efficient wireless access technology that can increase throughput without increasing the bandwidth or densifying the cell is essential to achieve the ongoing demands faced by 5G. Some of the significant advantages of 5G are:

• Data rate: 5G network would provide data rate up to 10 Gbps, which is almost a hundred times better than 4G networks.
• Latency: 5G network provides latency as low as 1 ms compared to 10 ms latency provided by 4G networks.
• Efficient signaling: 5G networks provide efficient signaling for IoT connectivity and M2M communication.
• User experience: 5G enhances augmented reality, virtual reality, and artificial intelligence.
• Spectral efficiency: 5G would provide ten times more spectral and network efficiency compared to 4G networks.
• Energy efficiency: 5G networks provide 90 % more efficient network energy usage compared to 4G networks.
• Ubiquitous Connection: 5G provides huge broadcasting data, which can support more than 65,000 connections, which is a hundred times more than 4G networks.
• Battery life: 5G provides almost ten years of battery life for low powered IoT devices.

Factors contributing to more increment in wireless data traffic.

Along with immense advantages, 5G technology comes with certain challenges. Some of the challenges for 5G technology are:

• Frequency bands: Frequency bands up to 300 GHz have been considered for 5G networks. These high-frequency bands are costly, and wireless carriers will have to pay millions to get this high-frequency spectrum.
• Coverage: The high-frequency wave has a shorter wavelength; thus, it cannot travel to a longer distance. Due to this issue, there should be more base stations in a smaller area to give each user a reliable connection. The additional base station increases the cost and complexity of the overall network.
• Cost: Since 5G is not just about adding an extra layer to the 4G network, the cost to build the system from the base level is prohibitive.
• Device Support: Since the phones available in the current market does not support 5G infrastructure, and it would be a challenge for device manufacturers to develop cheaper phone which can support 5G.
• Security and Privacy: Although 5G uses the authentication and Key Agreement (AKA) system, it is still venerable from attacks such as middle man attack, location tracking, and eavesdropping.
• Availability: With the introduction of M2M and IoT, network overload and congestion would be a major problem in the future. These radio access network challenges will make it difficult to make the network available to everyone.
• Cybercrime: With high speed, data Cybercrime would increase drastically. Thus, strict Cyberlaws would be necessary to prevent these attacks.

The 6G mobile networks are complete wireless networks with no limitation. It is currently in the developmental stage, and it will provide incredible transmission speed in the terabit range. This technology would require a smart antenna, large memory in cell phones, and huge optical networks. The 6G networks will be cell-free, and it would enable artificial intelligence in wireless networks. It is not clear what frequency band 6G networks will use, but it is apparent that a much higher frequency band will be needed to increase the data rate required for 6G networks. While 5G is supposed to use a frequency greater than 30 GHz and up to 300 GHz (millimeter waves), 6G is associated with much higher frequency in THz bands (300 GHz to 3 THz). The use of the THz spectrum for 6G is estimated to become commercial is the next 5–7 years. Some of the applications for 6G networks are connected robotics and autonomous systems, wireless brain-computer interfaces, blockchain technology, multi-sensory extended reality, space travel, deep-sea sightseeing, tactile internet, and industrial internet. 6G networks are expected to be introduced in the year 2030. Some of the advantages of 6G networks are:

• Data rate: 6G network is expected to provide data rate up to 10 Tbps, which is almost a hundred times better than 5G networks.
• Latency: 6G network would provide latency as low as 0.1 ms compared to 1 ms latency provided by 5G networks.
• Efficient signaling: 6G networks provide efficient signaling for massive IoT connectivity and M2M communication.
• User experience: 6G enhances extended reality, augmented reality, virtual reality, and artificial intelligence.
• Spectral efficiency: 6G would provide ten times more spectral and network efficiency compared to 5G networks.
• Energy efficiency: 6G networks provide 100 times more efficient network energy usage compared to 5G networks.
• Ubiquitous Connection: 6G will provide huge broadcasting data, which can support more than 1 million connections, which is almost a hundred times more than 5G networks.

Table 1 shows the feature comparison of 4G, 5G, and 6G networks.

Features of 6G Networks.

3. Key Enabling Technologies for 5G and Beyond Networks

To make 5G and beyond networks a reality, many advanced ideas have been proposed and analyzed in recent years. The major key enabling technologies that have been considered for 5G and 6G systems include millimeter waves, small cells, beamforming, device-centric architecture, full-duplex technology, massive MIMO, Terahertz wave, and visible light spectrum as shown in Figure 4 .

The 8 Key enabling technologies for 5G and beyond networks.

3.1. Millimeter Waves

Generally, a frequency below 6 GHz is used for cellular communication, and frequency above that is mostly used for other services like medical imaging, microwave remote sensing, amateur radio, terahertz computing, and radio astronomy. The massive increase in data traffic has made the radio frequency spectrum congested. The result is that there is limited bandwidth for a user, causing a slower and unreliable connection. One way to solve this problem is by using frequency above 6 GHz for wireless communication. The frequency above 6 GHz has never been used for wireless communication, and there has been a lot of research going on with broadcasting millimeter waves. Millimeter waves are frequency between 30 GHz to 300 GHz, and it is called millimeter waves because its length varies from 1 to 10 mm compared to the radio waves that are used in the current mobile communication system, which measure tens of centimeters in length.

Many aspects of millimeter waves are published in the past few years [ 10 , 11 ]. Authors in [ 12 , 13 ] discuss the potentials and challenges in the millimeter-wave technology. The future of the 5G network with millimeter wave technology is presented in [ 14 ]. Millimeter waves can provide bandwidth ten times more than that of the entire 4G cellular band. These high-frequency waves are used in some satellite application, but it has never been used for mobile broadband. Since millimeter has a lower wavelength, they are not suitable for long-range applications. Another problem with millimeter waves is that they cannot penetrate buildings and obstacles, and they tend to get absorbed by rain.

3.2. Sub-Millimeter or Terahertz Band

With globalization, the current wireless market is expanding rapidly. With talk of 6G networks, the demand for a higher spectrum is imminent in the near future. The frequency higher than the millimeter-wave band (30 GHz–300 GHz) could be used for wireless communication. The frequency band between 300 GHz to 3 THz is known as the Terahertz band. Although this idea is relatively new, research in this area can be worthwhile for the wireless communication industry. Other than just a higher spectrum, there are many advantages of THz band, such as interference friendly deployment, scalability, enhanced security, availability of greenfield spectrum, low power consumption, a front-haul boost for the wireless network, small antennas size, and focused beams [ 15 ].

THz technology would be beneficial for applications such as imaging, spectroscopy, holographic telepresence, industry 4.0, and massive scale communications. There are several challenges and new areas of research in THz band deployments such as complex antenna design to support higher antenna gain, access point specification and deployment, complex circuit design, high propagation loss, and complex mobility management [ 15 ]. The millimeter-wave and terahertz wave bands are shown in Figure 5 .

Millimeter and terahertz wave band.

The concept of ultra massive MIMO (UM-MIMO) has emerged in recent years, which takes advantage of plasmonic materials for building antennas and transceivers to achieve the capacity of THz band. Materials such as graphene and metamaterials can be used to build nano antennas and transceivers. These nano antennas and transceivers can operate in the THz band [ 16 ]. UM-MIMO can take advantage of these miniature antennas and transceivers to provide higher spatial multiplexing and beamforming. Thus, the data rates and communication range can be improved with the help of spatial multiplexing and beamforming, respectively. A lot of investigation is needed to realize THz UM-MIMO for 5G and beyond networks. Some of the challenges are the fabrication of plasmonic nano array antennas, channel estimation, precoding, signal detection, beamforming, and beemsteering [ 16 , 17 ].

3.3. Small Cells or Heterogeneous Networks

Small cells are low power tiny base stations that can be placed within every 100 m distance to cover small geographical areas. These low power base stations prevent the signal from dropping in crowded areas. Small cells are very light and small; thus, they can be placed anywhere. If we are using millimeter waves instead of the traditional sub-6 GHz spectrum, the small cell can become even smaller and can be fitted in tiny places. The small cells will play a significant role in delivering high-speed mobile broadband and ultra-low latency for 5G. Small Cells can be further divided into microcells, femtocells, and picocells based on coverage area and the number of users it can support. Several studies of smalls cells and its benefits for 5G networks are studied in [ 18 ].

3.4. Beamforming

Beamforming is the ability of the base station to adapt the radiation pattern of the antenna [ 19 ]. Beamforming helps the base station to find a suitable route to deliver data to the user, and it also reduces interference with nearby users along the route [ 20 ], as shown in Figure 6 . Beamforming has several advantages for 5G networks and beyond. Depending upon the situation, beamforming technology can be implemented in several different ways in future networks. For massive MIMO systems, beamforming helps with increasing spectrum efficiency, and for millimeter waves, it helps in boosting data rate. In massive MIMO systems, the base station can send data to the user from various paths, and beamforming here choreographs the packet movement and arrival time to allow more users to send data simultaneously. Since the millimeter waves cannot penetrate through obstacles and do not propagate to longer distances due to a shorter wavelength, beamforming here helps to send concentrated beams towards the users. Thus, beamforming helps a user to receive a strong signal without interference with other users.

Massive Multiple Output–Multiple Output (MIMO) beamforming.

3.5. Device Centric Architecture

The current 4G system relies on base station centric architecture where a device relies on downlink and uplink connection and control and data channel to obtain the services from the base station. With an increased number of users, cell density or base station density is increasing rapidly, and this densification in the network would require major changes in the 5G and beyond networks. Also, with the introduction of millimeter waves, many frequency bands with entirely different propagation characteristics will coexist together. Thus a base station centric architecture might evolve into a device-centric architecture in future networks to overcome challenges like network densification and increased frequency bands [ 21 ].

In device-centric architecture, a user device would communicate by exchanging information through several heterogeneous nodes [ 22 ]. Various research on the benefits of device-centric architecture for 5G networks is presented in Reference [ 23 ]. A typical device-centric architecture is shown in Figure 7 .

Device centric architecture.

3.6. Full Duplex Technology

Generally, wireless transmission and reception are not done at the same frequency bands to avoid interference. Any bidirectional system thus has to separate the uplink and downlink channel using time or frequency domain to get orthogonal non-interfering signals. Full duplex refers to the simultaneous transmission and reception over the same frequency band and at the same time, as shown in Figure 8 . 5G networks will use full-duplex for the transmission of signals to potentially double the network capacity and is beneficial for higher layers (e.g., MAC layer). One of the disadvantages of full-duplex technology is that it increases signal interference thought pesky echo [ 24 ]. Several studies have been conducted on full-duplex technology and its benefits for 5G networks [ 25 , 26 ].

Full duplex technology.

3.7. Visible Light Communication

Visible Light Communication (VLC) provides optical fiber like performance for future generation networks. It uses visible light between 400 and 800 THz using both fluorescent lamps or LEDs to transmit the signal over the shorter distance. VLC can be built with very low-cost hardware, and it can take advantage of the unlicensed band. VLC does not induce any electromagnetic radiation, which makes it unexposed to external electromagnetic radiation. Since this technology requires an illumination source, this technology is mostly useful for indoor applications. A standard for VLC has been defined in IEEE 802.15.7, but 3rd Generation Partnership Project (3GPP) has not considered it for cellular networks [ 27 ]. VLC would be very useful for smart city applications, and it has been recognized as one of the key enabling technologies for 6G networks.

3.8. Massive MIMO

MIMO systems are an integral part of current wireless systems, and in recent years they have been used extensively to achieve high spectral efficiency and energy efficiency. Before the introduction of MIMO, single-input-single-output systems were mostly used, which had very low throughput and could not support a large number of users with high reliability. To accommodate this massive user demand, various new MIMO technology like single-user MIMO (SU-MIMO) [ 28 , 29 ], multi-user MIMO (MU-MIMO) [ 30 , 31 , 32 , 33 ] and network MIMO [ 34 , 35 ] were developed. However, these new technologies are also not enough to accommodate the ever-increasing demands. The wireless users have increased exponentially in the last few years, and these users generate trillions of data that must be handled efficiently with more reliability.

Additionally, there are billions of IoT devices, having various applications to smart health-care, smart homes, and smart energy, that contribute to the data traffic. It is predicted that there will be around 50 billion connected devices by the end of 2020. The current MIMO technologies associated with 4G/LTE network is unable to handle this huge influx in data traffic with more speed and reliability. Thus, the 5G network is considering massive MIMO technology as a potential technology to overcome the problem created by massive data traffic and users [ 6 , 36 ]. Several studies on massive MIMO have been conducted on massive MIMO systems and their benefits [ 7 , 37 ].

Massive MIMO is the most captivating technology for 5G and beyond the wireless access era. Massive MIMO is the advancement of contemporary MIMO systems used in current wireless networks, which groups together hundreds and even thousands of antennas at the base station and serves tens of users simultaneously [ 38 , 39 ]. The extra antennas that massive MIMO uses will help focus energy into a smaller region of space to provide better spectral efficiency and throughput. Massive MIMO downlink and the uplink system is shown in Figure 9 . As the number of antenna increases in a massive MIMO system, radiated beams become narrower and spatially focused toward the user. The beam patterns for different antenna configurations are shown in Figure 10 . These spatially focused antenna beams increase the throughput for the desired user and reduce the interference to the neighboring user [ 40 ]. Massive MIMO offers an immense advantage over the traditional MIMO system, which are summarized in Table 2 [ 41 ].

Massive MIMO uplink and downlink.

Beam Pattern with different antenna configuration. ( a ) 4 × 4 MIMO ( b ) 16 × 16 MIMO ( c ) 32 × 32 MIMO ( d ) 64 × 64 MIMO.

Comparison of Traditional MIMO and Massive MIMO System.

3.8.1. Uplink Transmission

The uplink channel is used to transmit data and the pilot signal from the user terminal to the base station, as shown in Figure 11 a. Let us consider a massive MIMO uplink system equipped with M antennas at the base station and simultaneously communicating with N (M ≫ N) single-antenna users. If the signal transmitted by the user or the deterministic pilot signal to estimate the channel is x ∈ C N , the signal received at the base station during uplink is given as:

where y ∈ C M is the signal received at the base station, H is the channel vector between the user terminal and the base station, and elements of H ∈ C M × N are independent and identically distributed with zero mean and unit variance, that is, H ∼ CN ( 0 , 1 ) . The additional term n u p l i n k ∈ C M is the addition of interference from several transmissions and the receiver noise. The interference added is independent of the user signal x , but it can be dependent on the channel H .

Massive MIMO uplink and downlink operation. ( a ) Uplink ( b ) Downlink.

3.8.2. Downlink Transmission

The downlink channel is used to transmit data or estimate the channel between user and base station. The base station uses training pilots to estimate the channel. A downlink transmission with several UE and a base station is shown in Figure 11 b. Let us consider a downlink massive MIMO system, where base station equipped with M antennas, and it is serving N users having a single antenna simultaneously. The base station sends independent information to multiple users simultaneously. The signal received, y k ∈ C M × 1 at the k t h user is:

where h k is a channel vector between k t h user and base station, whose elements are independent and identically distributed with zero mean and unit variance, that is, h ∼ CN ( 0 , 1 ) . x k ∈ C M is the signal transmitted by base station for user k and, n d o w n l i n k is the additional noise which is composed of the receiver noise n n o i s e ∼ CN ( 0 , σ 2 I ) and the interference during downlink n d o w n l i n k − i n t e r f e r e n c e caused by transmitting simultaneously to other users and is given as:

4. Benefits of Massive MIMO for 5G Networks and Beyond

Some of the benefits of massive MIMO technology are:

• Spectral Efficiency: Massive MIMO provides higher spectral efficiency by allowing its antenna array to focus narrow beams towards a user. Spectral efficiency more than ten times better than the current MIMO system used for 4G/LTE can be achieved.
• Energy Efficiency: As antenna array is focused in a small specific section, it requires less radiated power and reduces the energy requirement in massive MIMO systems.
• High Data Rate: The array gain and spatial multiplexing provided by massive MIMO increases the data rate and capacity of wireless systems.
• User Tracking: Since massive MIMO uses narrow signal beams towards the user; user tracking becomes more reliable and accurate.
• Low Power Consumption: Massive MIMO is built with ultra lower power linear amplifiers, which eliminates the use of bulky electronic equipment in the system. This power consumption can be considerably reduced.
• Less Fading: A Large number of the antenna at the receiver makes massive MIMO resilient against fading [ 42 ].
• Low Latency: Massive MIMO reduces the latency on the air interface [ 43 ].
• Robustness: Massive MIMO systems are robust against unintended interference and internal Jamming. Also, these systems are robust to one or a few antenna failures due to large antennas [ 44 ].
• Reliability : A large number of antennas in massive MIMO provides more diversity gain, which increases the link reliability [ 45 , 46 ].
• Enhanced Security: Massive MIMO provides more physical security due to the orthogonal mobile station channels and narrow beams [ 47 ].
• Low Complex Linear Processing: More number of base station antenna makes the simple signal detectors and precoders optimal for the system.

5. Why Is Massive MIMO Becoming More Important for 5G Networks and beyond?

Since the Massive MIMO concept was introduced a few years ago, it has gained new heights every year. It has become one of the hottest research topics in the wireless communication community due to its immense benefits in 5G standardization. The current MIMO systems have been unable to cope with the massive influx in wireless data traffic. With the introduction of concepts like IoT, machine to machine communication, virtual reality, and augmented reality, the current system is unable to deliver the required spectral efficiency. The recent experiments in the massive MIMO system have proven its worth by showing record spectral efficiency. A research conducted by Lund University together with Bristol University in 2015 achieved 145.6 bits/s/Hz spectral efficiency for 22 users, each modulated with 256-Quadrature Amplitude Modulation (256-QAM), on a shared 20 MHz radio channel at 3.51GHz with 128 antennas at the base station [ 48 , 49 ]. Figure 12 shows the 100 antennae massive MIMO testbed created by Lund University in 2015. The improvement in spectral efficiency was huge when compared with 3 bit/s/Hz, which is International Mobile Telecommunications (IMT) advanced requirement for 4G.

An assembled 100-antenna massive MIMO test bed.

The efficient operation of massive MIMO systems has been validated in various environments, both indoor and outdoor. It has also been proven that the massive MIMO system provides a robust operation with low complexity radio frequency and baseband circuit [ 50 ]. The hardware implementation of a massive MIMO system also have been tested successfully, and it was proven that these systems could be built with very low complex and low-cost hardware for both digital baseband and analog RF chains [ 50 ]. Moreover, many precoding, detection, scheduling, and equalization algorithms have been designed to reduce cost and power further. All these new innovations and development in massive MIMO promote an attractive deployment of this technology required for 5G and beyond wireless networks.

Massive MIMO has already been implemented in China and Japan within a 4G LTE context. SoftBank Group Corp. in Japan deployed massive MIMO in its network in 2016. In 2017, Vodafone and Huawei together did a real-world experiment to test Massive MIMO systems and achieved a speed of 717 Mbps. In 2018, Nokia produced a lightweight and power-efficient chipset for a massive MIMO antenna design, and it was called ReefShark chipset. This chipset could reduce the massive MIMO antenna size to half, and it has been considered as one of the promising technology for Massive MIMO deployment [ 51 ]. Samsung also demonstrated that massive MIMO could provide simultaneous high-speed video streaming without delay in a crowded place by experimenting at a crowded stadium in South Korea [ 52 ]. In January 2019, Sprint Mobile completed the world’s first 5G data call using 2.5 GHz and Massive MIMO on 3GPP 5G New Radio commercial Network [ 53 ].

Theoretically, Massive MIMO systems can have an infinite number of antennas at the base station. But usually, 64 to 128 have been used practically in massive MIMO base station. Recently, Sprint Network working along with companies like leaders Ericsson, Nokia, and Samsung Electronics have deployed 128 antennas massive MIMO systems (64 antennas to receive signal and 64 antennas to transmit signal). One of the prominent advantages of massive MIMO is that we only need sophisticated hardware at the base station, while the UE can have a single antenna and a simple antenna design. Thus, for massive MIMO higher number of the antenna is only needed at the base station but not at UE. The current smartphones have 2 to 4 antennas. The current smartphones have 2 to 4 antennas, but for massive MIMO, having only one antenna at the UE will suffice.

6. Challenges in Massive MIMO and Mitigation Techniques

The massive MIMO technology is more than just an extension of MIMO technology, and to make it a reality, there are still many issues and challenges that need to be addressed. Some of the fundamental challenges in massive MIMO systems are shown in Figure 13 .

Challenges in massive MIMO deployment.

6.1. Pilot Contamination

In massive MIMO systems, the base station needs the channel response of the user terminal to get the estimate of the channel. The uplink channel is estimated by the base station when the user terminal sends orthogonal pilot signals to the base station. Furthermore, with the help of channel reciprocity property of massive MIMO, the base station estimates the downlink channel towards the user terminal [ 45 ]. If the pilot signals in the home cell and neighboring cells are orthogonal, the base station obtains the accurate estimation of the channel. However, the number of orthogonal pilot signals in given bandwidth and period is limited, which forces the reuse of the orthogonal pilots in neighboring cells [ 54 ]. The same set of orthogonal pilot used in neighboring cells will interfere with each other, and the base station will receive a linear combination of channel response from the home cell and the neighboring cells. This phenomenon is known as pilot contamination, and it limits achievable throughput, as shown in Figure 14 [ 55 ]. During downlink, the base station will beamform towards the user in its home cell along with undesired users in the neighboring cells. The effect of pilot contamination on system performance has been studied in [ 56 , 57 ].

Massive MIMO pilot contamination effect.

There are several techniques designed to mitigate the effect of pilot contamination in massive MIMO systems. The pilot based estimation approaches are presented in References [ 58 , 59 ]. These pilot based estimation methods show a significant gain when a large number of antennas are used at the base station. The subspace-based estimation approach to mitigate pilot contamination is studied in Reference [ 60 ], and it is considered as one of the best methods to increase spectral efficiency because this method required less number of orthogonal pilots. The pilot reuse mitigation scheme is presented in Reference [ 61 ], and a partial sounding resource reuse scheme is presented in Reference [ 62 ], and these methods are found to be effective in reducing pilot contamination in large antennas systems. A pilot contamination precoding scheme is presented in Reference [ 63 ], in which the base station receives the linear combination of signals from all the users using the same orthogonal pilot signal. A blind pilot decontamination method is described in References [ 64 , 65 ] using non-linear receivers. Although blind methods provided accurate channel estimation, its assumption that all the desired channels are stronger than the interfering channel does not always hold [ 66 ]. A pilot assignment based scheme and pilot decontamination using interference alignment have been presented in References [ 67 , 68 ]. Some other optimal methods for pilot contamination reduction system designs have been presented in References [ 69 , 70 ]. The author of Reference [ 71 ] presented an optimal pilot reuse factor based scheme based upon the user environment to ensure that system always operates at maximal spectral efficiency.

6.2. Channel Estimation

For signal detection and decoding, massive MIMO relies on Channel State Information (CSI). CSI is the information of the state of the communication link from the transmitter to the receiver and represents the combined effect of fading, scattering, and so forth. If the CSI is perfect, the performance of massive MIMO grows linearly with the number of transmitting or receive antennas, whichever is less [ 72 ]. For a system using Frequency Division Duplexing (FDD), CSI needs to be estimated both during downlink and uplink. During uplink, channel estimation is done by the base station with the help of orthogonal pilot signals sent by the user terminal. And during the downlink, the base station sends pilot signals towards the user, and the user acknowledges with the estimated channel information for the downlink transmission. For a massive MIMO system with many antennas, the downlink channel estimation strategy in FDD becomes very complex and infeasible to implement in real-world applications. Figure 15 a shows the FDD and Time Division Duplexing (TDD) mode in wireless communication, and Figure 15 b shows the typical pilot transmission and CSI feedback mechanism in FDD and TDD mode.

( a ) Frequency Division Duplexing (FDD) and Time Division Duplexing (TDD) mode: Massive works best in TDD mode. ( b ) Typical pilot transmission and CSI feed back mechanism in FDD and TDD mode.

TDD provides the solution for the problem during downlink transmission in FDD systems. In TDD, by exploiting the channel reciprocity property, the base station can estimate the downlink channel with the help of channel information during uplink. During uplink, the user will send the orthogonal pilot signals towards the base station, and based on these pilot signals, the base station will estimate the CSI to the user terminal [ 54 ]. Then, using the estimated CSI, the base station will beamform downlink data towards the user terminal. Since there is a limited number of orthogonal pilots that can be reused from one cell to another, the pilot contamination problem arises and is a significant challenge during massive MIMO channel estimation. Other challenges are increased hardware and computational complexity due to more number of antennas. Thus, low complexity and low overhead channel estimation algorithm are very desirable for massive MIMO systems [ 73 ].

Recently many algorithms have been designed for channel estimation in massive MIMO systems. A low complex Least Square (LS) estimation is presented in Reference [ 74 ], but the accuracy of the method is not optimal. Linear Minimum Mean Square Error (MMSE) algorithm is proposed in References [ 75 , 76 ] and several improvements of the MMSE algorithm are discussed in References [ 77 , 78 ]. Although MMSE provides optimal accuracy, the computational complexity is increased with more number of antennas. The complexity increases due to the large matrix inversion required by the algorithm. The channel estimation based on deep neural networks is presented in Reference [ 79 ], which eliminates pilot contamination under certain conditions. The blind channel estimation method is proposed in References [ 80 , 81 ], which are based on subspace properties of the received signal. Compressed Sensing (CS) based channel estimation is proposed in References [ 82 , 83 ], which further improves the downlink channel estimation. Massive MIMO iterative channel estimation and decoding is presented in Reference [ 84 ] to improve the complexity performance. Several other optimal methods have been presented recently to address the issue of channel estimation is massive MIMO [ 85 , 86 , 87 , 88 , 89 , 90 ]. Although massive MIMO is envisioned to use TDD operation, much research has been going on to use FDD operations in massive MIMO systems.

6.3. Precoding

Precoding is a concept of beamforming which supports the multi-stream transmission in multi-antenna systems. Precoding plays an imperative role in massive MIMO systems as it can mitigate the effect created by path loss and interference, and maximizes the throughput. In massive MIMO systems, the base station estimates the CSI with the help of uplink pilot signals or feedback sent by the user terminal. The received CSI at the base station is not uncontrollable and not perfect due to several environmental factors on the wireless channel [ 91 ]. Although the base station does not receive perfect CSI, still the downlink performance of the base station largely depends upon the estimated CSI.

Thus, the base station uses the estimated CSI and the precoding technique to reduce the interference and achieve gains in spectral efficiency. The performance of downlink massive MIMO depends upon the accurate estimation of CSI and the precoding technique employed. Although the precoding technique provides immense benefits to massive MIMO systems, it also increases the computational complexity of the overall system by adding extra computations. The computational complexity increases along with the number of antennas. Thus, low complex and efficient precoders are more practical to use for massive MIMO systems. Figure 16 shows the precoding in massive MIMO systems with M-antenna base station and N-users.

Precoding in a massive MIMO system with M antennas at base station communicating with N users.

Many linear and non-linear precoders have been proposed for massive MIMO systems. Although the non-linear precoders like Dirty Paper Precoding (DPP) [ 92 ], Tomlinson Harashima precoding (TH) [ 93 , 94 ], and Vector Perturbation (VP) [ 95 ] provide better performance, these methods have very high computational complexity when we have large antenna system. The linear precoders such as Maximal Ratio Combining (MRC) [ 96 ], Zero-Forcing (ZF) [ 97 , 98 ], Regularized ZF (R-ZF) [ 99 ], Water Filling (WF) [ 100 ], and MMSE [ 101 , 102 ] have lower computational complexity and can achieve near-optimal performance.

6.4. User Scheduling

Massive MIMO equipped with a large number of antennas at the base station can communicate with multiple users simultaneously. Simultaneous communication with multiple users creates multi-user interference and degrades the throughput performance. Precoding methods are applied during the downlink to reduce the effect of multi-user interference, as shown in Figure 17 . Since the number of antennas is limited in massive MIMO base station, if the number of users becomes more than the number of antennas, proper user scheduling scheme is applied before precoding to achieve higher throughput and sum rate performance.

Massive MIMO user scheduling.

There have been numerous studies in the last few years to find an optimal scheduling algorithm for massive MIMO [ 103 , 104 ]. Several linear methods such as ZF and MMSE provide near-optimal throughput performance and have acceptable computational complexity [ 105 , 106 ]. The non-linear methods such as Dirty Paper Coding (DPC) and Maximum Likelihood (ML) provide near-optimal performance, but they have higher computational complexity for a large number of antenna [ 92 ]. Several user scheduling algorithms have been proposed to improve the sum capacity, but computational complexity was not improved for a large number of antennas [ 107 , 108 ]. The Round-Robin (RR) [ 109 ], Proportional Fair (PF) [ 110 ], and Greedy algorithm [ 111 ] guarantee fairness among user. Still, they do not provide optimal throughput performance for massive MIMO systems with a large number of antennas. Multi-user scheduling and joining user scheduling methods have been proposed recently to provide optimal scheduling in a massive MIMO downlink system [ 112 , 113 ]. Several other efficient scheduling methods are proposed in [ 114 , 115 ].

6.5. Hardware Impairments

Massive MIMO system depends upon a large number of antennas to reduce the effect of noise, fading, and interference. A large number of antennas in massive MIMO increases the system complexity and increases the hardware cost. To deploy massive MIMO, it should be built with low cost and small components to reduce the computational complexity and hardware size. The use of a low-cost component will increase the hardware imperfections such as phase noise, magnetization noise, amplifier distortion, and IQ imbalance [ 116 ]. These imperfections have a major impact on overall system performance. Due to a large number of antennas, there is a mutual coupling between the antenna elements, which changes the load impedance and causes distortions [ 117 ]. Although massive MIMO promises to reduces the radiated power 100 times than of conventional MIMO systems, the power consumption by baseband hardware and data converters increases linearly with an increase in the number of antennas. Using low-cost phase-locked loop (PLL) and oscillators increases the phase shift between the time at which pilot and data signal is received at each antenna, which also limits the massive MIMO performance [ 6 ]. The hardware impairment at a massive MIMO base station is shown in Figure 18 .

Massive MIMO hardware impairments.

Although the hardware impairment cannot to completely removed, its influence can be mitigated with proper use of compensation algorithms. The use of hardware impairment algorithms like phase noise estimation and compensation and digital pre-distortion are infeasible with a large number of antennas, as computational complexity increases exponentially [ 118 , 119 ]. The phase shift problem can be significantly reduced by the design of smart transmission physical layer schemes. To reduce the cost of baseband signal processing, it is highly desirable to build dedicated hardware, which can also run in parallel. The impact of a low-cost amplifier on the transmitter can be mitigated by having a low Peak to Average Power Ratio (PAPR) [ 120 ].

6.6. Energy Efficiency

Energy efficiency is the ratio of spectral efficiency and the transmit power, and massive MIMO can provide substantial energy efficiency gains by achieving higher spectral efficiency with low power consumption. However, the increasing number of the antenna does always increase the spectral efficiency, because the power consumption also increases along with the number of antenna and more number of users. Based on this analogy, many studies have been carried out to build energy-efficient massive MIMO systems. Many low complex and low-cost methods for precoding, detection, channel estimation, and, user scheduling have been proposed recently to reduce the power consumption at the massive MIMO base station. Some researchers have focused on antenna and power amplifier design to reduce the power consumption of the system. In Reference [ 121 ], the authors proposed methods to reduce the mutual coupling induced distortion, but these methods are computationally inefficient for massive MIMO systems.

6.7. Signal Detection

In massive MIMO systems, due to a large number of antennas, the uplink signal detection becomes computationally complex and reduces the achievable throughput. Also, all the signals transmitted by users superimpose at the base station to create interference, which also contributes to the reduction of throughput and spectral efficiency. Figure 19 shows a massive MIMO system with N user terminal and M antenna at the base station. All the signals transmitted by N user terminal travel through a different wireless path and superimpose at the base station, which makes signal detection at the base station complex and inefficient. There has been extensive research to find the optimal signal detection method for massive MIMO systems that can provide better throughput performance with lower computational complexity. The conventional non-linear detectors like Sphere Decoder (SD) [ 122 ] and Successive Interference Cancellation (SIC) [ 123 ] yield good performance. Still, the computational complexity increases with more number of antennas, which makes them infeasible for massive MIMO systems.

An uplink massive MIMO system.

Several linear detectors have been considered for uplink detection in massive MIMO, such as ML, ZF, and MMSE [ 47 , 124 ]. ML is an optimal detector in massive MIMO, and it minimizes the probability of error, but for large antennas systems, the algorithm has prohibitive complexity [ 125 , 126 ]. The ZF methods mitigate the inter-antenna interference, but for ill-conditioned channel matrices, additive noise gets increased [ 127 ]. The MMSE detector has better performance than the ZF detector as it also considers the noise power during the detection [ 128 ]. Although the ML, MMSE, and ZF detection algorithms provide optimal throughput performance, they involve matrix inversion during the processing, which makes them computationally inefficient for large antenna massive MIMO systems. The ZF and MMSE algorithms combined with the Successive Interference Cancellation (SIC) method were considered to cancel the interference from previously detected symbols [ 129 ]. For low complexity signal detection of massive MIMO systems, several iterative methods have been designed [ 130 , 131 ]. Neumann Series Approximation (NSA) method [ 132 ], Richardson method [ 133 ], Successive Over-Relaxation Method (SOR) [ 74 ], and Jacobi Iterative Method [ 134 ] have been considered, but computational complexity was slightly reduced, when compared to conventional linear methods. Other linear methods such as Gauss Siedel (GS) [ 135 ], Conjugate Gradient (CG) [ 131 ], Least-square regression selection [ 136 ], Huber fitting based Alternating Direction Method of Multipliers (ADMM) [ 137 ], and Approximate Message Passing (AMP) [ 138 ] methods were also considered for massive MIMO, but they were also not found optimal for massive MIMO uplink detection. Several other optimal algorithms for massive MIMO uplink signal detection are presented in References [ 86 , 139 , 140 , 141 , 142 , 143 ].

7. Can Our Current Mobile Phones Use Massive MIMO Technology?

Our current phones do not support massive MIMO systems, and you cannot buy a massive MIMO ready phone yet. Even if you buy a phone which supports massive MIMO, it will not be beneficial until we have massive MIMO supporting wireless network. However, many phones now benefit from MIMO technology to achieve higher data rates and reliability. Every antenna embedded on the phone is used for transmitting and receiving the data. The added number of the antenna means, your device can send and receive more data at once. Hence this will boost the upload and download speeds. Today, most of the flagship phones come up with 4 × 4 MIMO, and they are two times faster than the phones having 2 × 2 MIMO as they will have two free antennas. Currently, iPhone XR, iPhone X, and iPhone 11 are equipped with 2 × 2 MIMO whereas iPhone 11 pro, iPhone 11 pro-Max, iPhone XS Max, Samsung Galaxy S8/S9/S10, Google Pixel 2/Pixel 3, HTC U11/U12+, and Huawei Mate 20 Pro are some of the phones that support 4 × 4 MIMO [ 144 ]. Although your phone does not support the massive MIMO system, you can still get benefit from the massive system as the connection would be more reliable and sensitive. Overall, the reliable connection and higher data are always good to have, but you have to pay some extra bucks to use massive MIMO technology.

8. Machine Learning and Deep Learning for Massive MIMO Systems

Machine learning is a subset of artificial intelligence, which is known as a powerful tool for classification and prediction problems. Deep learning is a subset of machine learning, and it uses more advanced tools capable of building universal classifiers and approximate general functions. These new concepts have been widely used in areas such as natural language processing, network security, and automated systems (autonomous cars). Currently, both machine learning and deep learning are very crucial technology for the design of 5G and 6G networks. Massive MIMO requires very complex optimizations, and the traditional algorithm, such as stochastic geometry and game theory, are very sophisticated and require enormous computing power. The dynamic nature of machine learning and deep learning algorithms could be instrumental for there complex analysis, and it could save a considerable amount of computational power [ 145 ]. These machine learning and deep learning algorithms are useful during massive MIMO beamforming, channel estimation, signal detection, load balancing, and optimization of available spectrum [ 146 , 147 ]. The uses of deep learning and machine learning for massive MIMO have been studied in [ 145 , 148 ].

During channel estimation, channel data can be considered as big data, and several machine learning tools can be used to predict massive MIMO channels. The accurate prediction of the channel via machine learning with significantly improve the throughput of massive MIMO systems. The use of machine learning or deep learning for channel estimation in massive MIMO is shown in Figure 20 . The authors of Reference [ 149 ] have used the Convolutional neural network (CNN) method for channel estimation, but the optimal performance was not achieved. CNN combined with a projected gradient descent algorithm was presented in Reference [ 150 ] that demonstrates the feasibility of using machine learning methods in channel estimation. The use of machine learning to estimate channel in complex channel model conditions has been studied in Reference [ 151 ]. Deep learning-based channel estimation has predicted more accurate channels compared to conventional channel estimation algorithms [ 152 ]. The authors of Reference [ 153 ] considered a massive MIMO channel as an image and applied a deep learning image super-position and denoising method. Various other research has been conducted to develop end to end Deep neural network (DNN) architecture to modify the modules at the base station and UE’s [ 154 ]. Deep learning-based channel estimation for various scenarios have been presented in Reference [ 155 ], and the results were like those of the optimal MMSE algorithm. Machine learning algorithms can reduce channel estimation overhead during CSI estimation in massive MIMO systems. Deep learning-based sparse channel estimation methods and their advantages over traditional estimation methods have been presented in Reference [ 90 ]. The CSI estimation problem in massive MIMO can be considered as time series learning problem by considering channel aging property of massive MIMO. The recurrent neural network (RNN) is a powerful tool to solve this time series learning problem. Since CSI estimation has distant data, simple RNN tools are less efficient in predicting the distant data in wireless communication. Thus, several architectures have been proposed recently to address this distant data problem in massive MIMO, such as long short-term memory (LSTM) and non-linear autoregressive network with exogenous inputs (NARX) [ 156 , 157 ]. Machine learning-based channel prediction in a massive MIMO system with channel aging property has been studied in Reference [ 158 ].

Massive MIMO channel estimation using machine learning and deep learning.

CNN combined with the autoregressive network (ARN), and RNN has been studied in Reference [ 158 ]. The machine learning assisted user scheduling method presented in Reference [ 159 ] provides a low complexity scheduling scheme for massive MIMO systems. The authors of Reference [ 160 ] presented a novel channel mapping in space and frequency using deep learning in massive MIMO. This novel solution reduces the training and feedback overhead in massive MIMO systems. Machine learning has also been used for efficient beam alignment in massive MIMO systems to track the users efficiently [ 161 ]. Several machine learning and deep learning techniques are also useful for uplink signal detection in massive MIMO. The conventional signal detection methods are computationally very complex and inefficient for large antennas systems like massive MIMO. Several semi-supervised learning (SSL) [ 162 ] and supervised learning (SL) [ 163 ] approach have been proposed and provide more robust performance. Several other uses of machine learning and deep learning have been presented in Reference [ 164 , 165 , 166 , 167 ].

9. Active Research Topics on Massive MIMO for 5G and beyond Networks

Although massive MIMO provides immense benefits, there are still various challenges such as pilot contamination, channel estimation, precoding, user scheduling, hardware impairments, energy efficiency, and signal detection that needs to be addressed and tested in a real-world environment before we can achieve its promised advantages. These deployment challenges in massive MIMO systems have pushed both academia and industry to focus on massive MIMO systems. Also, new technologies like massive MIMO, ultra massive MIMO, millimeter waves, terahertz waves, and visible light communication needs a lot of research before it gets implemented in our current wireless system. Some of the possible research topics in massive MIMO for 5G and beyond networks are:

• Massive MIMO system depends upon a large number of antennas to reduce the effect of noise, fading, and interference. A large number of antennas in massive MIMO increases the system complexity and increases the hardware cost. To deploy massive MIMO, it should be built with low cost and small components to reduce the computational complexity and hardware size. The low-cost equipment will increase the hardware imperfections such as phase noise, magnetization noise, amplifier distortion, and IQ imbalance. Although the hardware impairment cannot to completely removed, its influence can be mitigated with proper use of compensation algorithms. Design of these compensation algorithms is a good area of research in massive MIMO.
• Since there are limit number of orthogonal pilots that can be used in a particular time, the pilot contamination becomes one of the significant challenges in massive MIMO deployment. Pilot contamination increases interference and limits the achievable throughput. Several research has been conducted to mitigate the effect of pilot contamination. However, there is a need for an optimal method that mitigates its effect [ 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 ]. Thus, effective ways to mitigate the pilot contamination effect is an essential area to investigate.
• Although the precoding techniques increase throughput and reduce interference, it increases the computational complexity of the overall system by adding extra computations. This computational complexity increases with a large number of antennas. Thus, it is more practical to use low complex and efficient precoders in massive MIMO. Through investigation to find efficient precoding technique for massive MIMO is also an essential area of research.
• Since there are a limited number of antennas in the massive MIMO base station, user scheduling has to be performed if the number of the users is more than the number of antenna terminals at the base station. Massive MIMO system throughput can be increased by only scheduling the users experiencing good channel conditions. But using this scheme, the users at the edge of the cell with poor channel conditions are ignored and never scheduled. To improve overall system performance, a certain amount of fairness must be ensured among all the users. Several research has been conducted to achieve an efficient user scheduling algorithm [ 92 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 ], but optimal performance has not been achieved. Further research should be conducted to find a more efficient and fair scheduling algorithm design that can provide a higher data rate and guarantee fairness among users.
• In massive MIMO systems, due to a large number of antennas, the uplink signal detection becomes computationally complex and reduces the achievable throughput. Also, all the signals transmitted by users superimpose at the base station to create interference, which also contributes to the reduction of throughput and spectral efficiency. A recent experiment has achieved near-optimal performance, but more efficient algorithms are required to realize massive MIMO [ 47 , 74 , 86 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 ]. One of the crucial areas of investigation is to find more efficient and low complex uplink signal detection algorithm.
• Accurate CSI is needed in massive MIMO for beamforming data, detecting user signal, and resource allocation [ 168 ]. The user terminal has to estimate signal coming from a large number of antennas at the base station. Furthermore, the pilot overhead also increases drastically. Thus, an efficient channel estimation scheme with reasonable pilot overhead is an exciting area to investigate, particularly for FDD scheme.
• An exciting area for research in massive MIMO will be to combine it with quantum communication with a frequency higher than 300 GHz.
• Massive MIMO technology will be used for a user having a large number of antennas. Massive MIMO transceiver design, complexity, performance should be tested with users having a large number of antennas.
• Since the phones available in the current market does not support massive MIMO infrastructure; it would be a challenge for device manufacturers to develop cheaper phone which can support this technology. Design of a massive MIMO system that can integrate with the current 4G network is an excellent area to study.
• The use of machine learning and deep learning algorithms during massive MIMO channel estimation to predict statistical channel characteristics is an exciting area of research. Several experiments have been conducted recently to explore machine learning and deep learning for massive MIMO channel estimation, user scheduling, beamforming, and signal detection [ 90 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 , 167 ].
• The study on potential key enabling technologies for 6G networks such as THz communication, visible light communication, and holographic radio is also an interesting area to investigate.
• Further investigation is required to realize THz UM-MIMO for 5G and beyond networks. Some of the areas to the important area to investigate are the fabrication of plasmonic nano array antennas, optimal channel estimation methods, low complex and efficient precoding, and signal detection algorithms, accurate beamforming, and beemsteering [ 16 , 17 ].

Table 3 provides a summary of the massive MIMO system, its characteristics, benefits, and challenges. Table 4 summarizes the fundamental challenges in massive MIMO system implementation and recently proposed mitigation techniques.

Summary of Massive MIMO System, its Characteristics, Benefits, and Challenges.

Summary of Challenges and Mitigation Techniques in Massive MIMO System.

10. Conclusions

The need for an efficient cellular spectrum that can accommodate the tremendous surge in wireless data traffic is imminent. Massive MIMO wireless access technology is the answer to this global demand. Massive MIMO technology groups together antennas at both transmitter and the receiver to provide high spectral and energy efficiency using relatively simple processing. Given the worldwide need for an efficient spectrum, a limited amount of research has been conducted on massive MIMO technology. Thus, several open research challenges are still in the way of this emerging wireless access technology.

This paper provides an extensive overview of massive MIMO systems, highlighting the key enabling technologies for 5G and beyond networks. Although massive MIMO offers immense benefits for 5G and 6G networks, there are still various deployment challenges such as pilot contamination, channel estimation, precoding, user scheduling, hardware impairments, energy efficiency, and signal detection that needs to be addressed before we can achieve its promised advantages. Furthermore, this paper outlines the recent trends such as terahertz communication, UM-MIMO, VLC, and application of machine learning and deep learning technology for massive MIMO systems. We hope that this paper will motivate the researchers currently working on 5G and beyond networks field to find new paths and open problems to tackle in the coming years.

Acknowledgments

We want to thank the authors of the literature cited in this paper for contributing useful ideas to this study.

Abbreviations

The following abbreviations are used in this manuscript:

Author Contributions

The authors declare that they have equally contributed to the paper. All authors read and approved the final manuscript.

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Microwave technology continues to evolve to meet the challenges of 5G and AI data requests (Analyst Angle)

Mobile backhaul is a critical component of any mobile network, as it is the link supporting voice and data transport between the core network and Radio Access Network (RAN) sites. Wireless backhaul (i.e., microwave) continues to be a key technology — ABI Research estimates that around 60% of mobile cell sites are backhauled by microwave today — so this article examines some of the key developments and innovations in the microwave industry.

Key Industry Trends

• Demand for data capacity continues to grow

Annual 5G subscriptions from the top 30 countries monitored by ABI Research reached 1.5 billion at the end of 2023 and are expected to grow to over 4.1 billion by the end of 2029. Accordingly, 5G data traffic is also forecast to increase from over 340 exabytes in 2023 to 2,100 exabytes in 2029 (at a Compound Annual Growth Rate (CAGR) of 35%) as the growth in high-resolution video, both downloads and uploads, as well as Artificial Intelligence (AI)-enabled applications, ramp up.

• Challenges in addressing the digital divide

Beyond urban areas, Communication Service Providers (CSPs) also face the challenge of enhancing the quality of mobile connectivity in rural areas, where lower population density often means lower Return on Investment (ROI). This issue is a global one, affecting both developed and developing countries. For example, studies by Opensignal have shown the large gap in 5G mobile broadband download speeds and availability rates between urban and rural areas across multiple European countries. Similarly, challenges were also reported in Asian countries like Malaysia, Indonesia and the Philippines.

• Tower rentals imposing additional burdens on CSPs

Another key trend observed in the market is the continuing divestment of CSP tower assets to Tower Companies (TowerCos). Announcements of telco tower sales are continuing in developed markets and accelerating in emerging markets, with deals being executed across geographies, including the Philippines, South Africa, Bangladesh, India and Egypt. While microwave solutions typically have lower deployment costs compared to wired backhaul solutions, there are increasing concerns from CSPs regarding the rise of Operating Expenditure (OPEX) due to accumulating tower-related telco equipment rental costs. As a result, reducing wind load, quantity and weight of microwave equipment has become a key priority for both Network Equipment Vendors (NEVs) and CSPs alike.

• Network topology evolution to support network densification

“Star” topologies represent an evolution from the traditional “tree” topology where a central hub transceiver links to multiple small cells, with each small cell sharing the hub transceiver bandwidth. In this topology, for every N microwave link, only N+1 radios are required, giving rise to Capital Expenditure (CAPEX) and OPEX savings over the Point-To-Point (PTP) connectivity used in “tree” topologies. The “star” topology is intended for the backhaul of heavy mobile traffic sites in urban locations, where small cell densification is growing.

Meeting Backhaul Requirements with Microwave

According to the European Telecommunications Standards Institute (ETSI), 5G transport networks will need to meet capacity requirements of 3 Gigabytes per Second (Gbps) for rural, 5 Gbps for suburban, between 5 and 10 Gbps for urban and more than 10 Gbps for dense urban environments. Additionally, latencies of below 5 milliseconds (ms) and 1 ms must also be met for Enhanced Mobile Broadband (eMBB) services and Ultra-Reliable Low Latency Communications (URLLC) mission-critical applications, respectively. Many telcos have yet to meet these performance metrics.

5G-Advanced networks are also starting to be deployed. In June 2024, The 3rd Generation Partnership Project (3GPP) announced that Release 18 specifications — the first release for 5G-Advanced technology — was ready to be frozen, setting in place the necessary environment to facilitate large-scale commercial rollouts of 5G-Advanced-compliant solutions. Many operators around the world, including, but not limited to Optus (Australia), Maxis (Malaysia), du (United Arab Emirates) and China Unicom (China), have begun trials of the 5G-Advanced technology, thus signaling the growth potential for the technology. With 5G-Advanced expected to support even higher downlink and uplink speeds of up to 10 Gbps and 1 Gbps, respectively, the future requirements for mobile backhaul can only increase.

• E-Band continues to play an important role in supporting advanced networks

The E-band is, and continues to be, an important technology to support wireless mobile backhaul. First , operating in the higher frequency ranges of between 71 Gigahertz (GHz) and 86 GHz, and coupled with wide channel sizes, E-band wireless links can support more than 10 Gbps capacities with low latency, making it an ideal option for wireless backhaul in dense urban and suburban scenarios. Second , CSPs can also benefit from the generally lower spectrum costs of deploying E-band links, where a tiered spectrum pricing structure for licensed E-band links is commonly observed. Third , the capabilities of E-band solutions are also improving, with multiple vendors offering 1) high-power radios and high-gain antennas; 2) beam tracking; 3) Band and Carrier Aggregation (BCA); 4) Cross Polarization Interference Cancelling (XPIC); and 5) AI solutions to enhance the capacity, link distance and energy efficiency for the E-band.

In particular, XPIC is seen as a key technology for the E-band. XPICis done by propagating two signals horizontally and vertically over the same channel, increasing channel reusability and thereby overall link capacity. With the use of XPIC, E-band links can provide up to 20 Gbps capacities in urban environments. XPIC technology also reduces costs by removing the need for additional hardware equipment and optimizes spectrum usage efficiency. This technology is expected to become increasingly important to support high-capacity requirements, especially because the majority of E-band channel sizes around the world are less than or equal to 1,000 Megahertz (MHz).

• Traditional microwave bands remain essential for long-haul microwave backhaul links

Traditional microwave bands continue to play an important role in microwave networks, especially in supporting long-haul links for rural areas. Utilizing technologies such as BCA, which bonds different carriers and/or frequency bands together, microwave solutions can achieve higher capacity, range and reliability, while reducing equipment quantity, size and power consumption. However, a key consideration here remains how to introduce minimum footprint for on-tower equipment that optimizes data throughput and range, while keeping tower rental costs low.

• Higher aggregation capabilities on hub sites to address site densification

As opposed to fiber deployments, microwave links allow for more flexible deployments due to reduced complexity of installations and avoidance of potential right-of-way issues. However, to effectively address the increasing densification of cell sites in urban and dense urban areas, microwave hub/aggregation solutions need to support higher traffic aggregation capabilities with lower link spatial separations.

Microwave Technology Evolution Trends

To adequately address CSPs’ concerns on backhaul capacity and tower rental costs, microwave solutions continue to evolve to meet these changing needs.

• High-power and dual-carrier E-band solutions

To increase data capacities, higher-powered E-band solutions are being introduced with 25 Gigabit Ethernet (GE) interfaces. Additionally, to overcome the typical limit of 1,000 MHz for E-band channel sizes, there have been introductions of dual-carrier E-band solutions (2T Outdoor Units (ODUs)) that support data throughputs of up to 50 Gbps with the use of both dual-channel and dual-polarized transmissions, via a single unit of equipment. Such innovations effectively balance the need for increased data capacity, while keeping tower rental costs low.

• Innovations being developed for long-haul microwave backhaul links

While BCA is not a new concept, NEVs have continued to introduce new innovations to their long-haul microwave backhaul solutions to drive increased efficiencies and tower rental cost savings:

• Multi-band antennas address the issue of tower space and weight constraints by enabling a single antenna to support multiple frequency bands, as opposed to deploying a separate antenna for each frequency band. Recent innovations by backhaul vendors, such as Huawei’s SuperLink solution, have demonstrated how a single antenna can now support up to four frequency bands, thereby reducing the total number of antennas needed on the tower.
• Carrier aggregation enables a single radio to support multiple channels simultaneously, thereby enabling higher data throughput without the need for additional equipment. In this regard, several NEVs have launched 2T2R microwave Radio Frequency Units (RFUs) that can support up to four carriers on a single hardware unit.
• Band aggregation bonds different frequency bands together, such as the E-band with traditional microwave band links, to increase the range and reliability of higher-frequency Millimeter Wave (mmWave) bands. For example, by bonding a microwave band (e.g., the 18 GHz band) with the E-band, mobile operators can more than double the propagation range, with the link in the lower band being used to assure carrier-grade availability (i.e., 99.995%). The introduction of multi-band branches also helps reduce the quantity of equipment needed to support horizontal and vertical polarization for multi-band deployments.

New microwave hub/aggregation solutions that support frequency reuse at reduced spatial separations—as low as 15°—are being introduced to support “star” topologies. This reduced spatial separation allows CSPs to deploy more microwave links in more directions via a single hub site, thereby both reducing costs of deployment and increasing spectrum efficiency. Additionally, these hub solutions also feature high switching capacities and 25GE interfaces. As a result, CSPs will be able to deploy dense microwave networks quickly to address the increasing demand for data in suburban and dense urban areas.

Summary conclusions

In conclusion, microwave technology continues to evolve with the changing requirements of mobile access technology and is expected to maintain an important role in providing reliable mobile backhaul connectivity, as a standalone solution or as a redundancy option for fiber networks. With increasing demand for data and the burgeoning AI market, modern microwave solutions are designed to not only help CSPs meet the requirements for higher backhaul capacity, but to also reduce tower load and OPEX. CSPs will need to consider how they can modernize their backhaul networks to meet future mobile requirements.

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TCL TAB 10 NXTPAPER 5G: Establishing Display Technology as a Key Differentiator in the Mid-range Tablet Market

• / Aug 29, 2024
• / Gerrit Schneemann
• TCL is endorsing its NXTPAPER display technology as a key differentiator for its smartphones and tablets to address eye strain caused by increasing screen time.
• 5G connectivity is included but adds little to the device’s overall usability – at least for users in mature markets where the device is likely to be a secondary device.
• Carrier data device pricing remains prohibitive and can add significant cost to a potential purchase, especially in the mid-range segment. More flexibility in pricing, data allowance and accessibility of standalone plans would go a long way to drive adoption.

To stand out in the tablet market’s crowded lower-mid-range segment is no small feat. TCL, known for its willingness to chart a different course than the competition, has made a compelling move with the launch of the TCL TAB 10 NXTPAPER 5G connected tablet for Verizon in the US. The TAB 10 is not just another addition to the 5G-connected tablet market. Rather, the device features TCL’s unique NXTPAPER display technology, setting it apart from the competition.

TCL has been focusing on NXTPAPER over the last year or so. The key selling point of the technology is the ability to mimic paper-like texture and change color settings dynamically to create the feel of an e-reader and minimize eye strain.

The benefits of the technology are clearly noticeable when using the device. Simultaneously, the challenges the form factor continues to face are also not hard to miss, particularly in the way carriers continue to try to monetize integrated 5G connectivity.

NXTPAPER – Standout Branding Opportunity

The TCL TAB 10 NXTPAPER 5G’s standout feature is its NXTPAPER display technology. Unlike traditional LCD or OLED displays, NXTPAPER combines a multi-layered screen with a reflective layer that reduces glare and mimics the experience of interacting with an e-ink reader, like Amazon’s Kindle. The technology enhances readability by filtering out harmful blue light and minimizing eye strain. Blue light strain is a challenge many OEMs have been trying to address across smartphones and tablets. TCL’s unique solution is based on its in-house capabilities as a display maker.

The innovative technology allows users to toggle between a traditional color display and two e-ink display modes – one with color and one for black and white viewing. This makes the TCL TAB 10 NXTPAPER 5G especially attractive to students and families who need a device that can handle a variety of tasks and app use cases. A benefit of the viewing modes is that Android apps continue to function as intended, just adjusted for the color theme – including watching videos and changing the display mode with a video rolling in the background.

TCL’s Strategic Positioning in the Mid-range Market

TCL’s integration of NXTPAPER technology in the TAB 10 5G is a strategic move to differentiate itself in the saturated lower mid-range market. At Verizon, the tablet costs $239.99, or can be purchased via a payment plan, like for smartphones. Users will not find high-powered cameras or build quality of a premium tablet. The integrated haptic feedback is harsh and distracting. The bezels are large and do not look as modern as the integrated 5G connection would suggest the device to be.

However, the changing display modes provide enough of a new user experience to keep consumers engaged with the device for some time. The ability to turn longer online articles into e-ink books proved to be an enjoyable experience and changes how users interact with text. For children’s texts, the color e-ink mode creates a physical book-like experience.

The anti-glare characteristics of the display worked to an extent but did not completely solve the issue. In bright light, there still were instances where visibility was limited, just like with regular displays.

The Challenge of 5G Connectivity

One of the standout features of the tablet, 5G connectivity, is also one that creates hurdles for buyers. The tablet is compatible with Verizon’s network and buyers can add it to an existing Verizon phone plan for as low as $20/month, before other discounts, for unlimited data access. However, non-Verizon subscribers have to effectively subscribe to one of Verizon’s Unlimited plans, usually meant for phones, for $90/month.

For buyers of a mid-range tablet, even an add-on discounted $10/month access fee to other phone plans attaches $120 per year to their bill – on a device that costs under $240 outright. Combined with generous hotspot allowances on phone plans, this pricing dynamic illustrates why carriers and OEMs continue to struggle to make 5G a key feature for tablets, and by extension laptops as well. Adding 5G connectivity to a tablet, especially in mature smartphone markets, remains expensive and does not solve problems for users.

The TCL TAB 10 NXTPAPER 5G represents TCL’s commitment to innovation, especially in display technology. By leveraging its proprietary NXTPAPER display technology and combining it with 5G capabilities, TCL’s TAB 10 tries to address a number of user pain points – some more so than others.

The NXTPAPER technology works to turn the tablet into an e-ink reader, with advanced capabilities. The display reduces eyestrain and also makes content more visible in bright sunlight.

However, 5G connectivity does not add enough value to warrant the costly data add-on for existing Verizon users. For new users, purchasing a mid-range tablet and opening a new line of service with Verizon would cost over $90/month – negating the affordability of the tablet and posing as key hurdles to 5G adoption in mature smartphone markets in other device form factors.

TCL’s approach to the tablet space positions the company well as an affordable alternative to Samsung and Apple. However, carrier pricing strategies remain a key hurdle and TCL should embrace other retail channels to expose more potential buyers to one of its key differentiators in smartphones and tablets – its NXTPAPER display technology.

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