The capacity of a wireless link is generally measured in bits per second per Hertz (b/s/Hz). The methods available to increase this capacity in a traditional Single Input, Single Output (SISO) wireless system are fairly limited: increase the bandwidth, allowing a corresponding increase in the bits per second, or increase the transmit power, allowing a higher level modulation scheme to be utilized for a given bit error rate, effectively increasing the bits per second within the same bandwidth.
The problem with both of these techniques is that any increase in power or bandwidth can negatively impact other communications systems operating in adjacent spectral channels or within a given geographic area. As such, bandwidth and power for a given communications system are generally well regulated, limiting the ability of the system to support any increase in capacity or performance.
MIMO technologies overcome the deficiencies of these traditional methods through the use of spatial diversity. Data in a MIMO system is transmitted over T transmit antennas through what is referred to as a “MIMO channel” to R receive antennas supported by the receiver terminal. MIMO has the ability to significantly increase raw data throughput in spectrally limited environments, while at the same time providing immunity to the multipath effects common in urban settings.
If the antennas within the transmit array and the antennas within the receive array are spaced sufficiently far apart, the signals traveling between the various transmit and receive antennas through the MIMO channel will fluctuate or fade in an independent manner. The transmitted data can therefore be encoded, using a so-called space-time code, to make use of this spatial diversity and allow processing at the receiver to extract the underlying data.
The specific coding scheme utilized in the MIMO system is selected based on the target performance, the acceptable level of computational complexity in the receiver’s signal processing subsystem, and the level of a priori knowledge of the transmission channel.
In space-time diversity coding, each modulated symbol is encoded and transmitted from each of the transmit antennas. This maximizes the total available spatial diversity from the MIMO channel, on a per symbol basis, offering a significant increase in bit error rate performance over an equivalent SISO channel operating at the same transmit power. Space-time diversity coding works with any number of transmit or receive antennas, with the total diversity order equal to T*R. Space-time diversity codes support a symbol rate of at most one symbol per symbol period.
Various space-time coding schemes have been developed for use in space-time diversity coding. In one of the earlier schemes, referred to as Delay Diversity, each symbol sent on one antenna is delayed by a symbol period and then sent on another antenna. This scheme is a simple example of a space-time trellis code (STTC), and is typically decoded through the use of a fairly complex maximum likelihood sequence estimator in the front end of the receiver. The more popular scheme for space-time diversity coding is the Alamouti scheme. This scheme utilizes a simple space-time block code (STBC) that encodes two modulated symbols into a matrix that is two rows by two columns in size. During each symbol period, the contents of a row are transmitted via the corresponding antennas.
The improvement in signal to noise ratio at the receiver using space-time diversity coding can be quite high, with one paper reporting up to 16dB improvement for a two transmit and two receive antenna system. This improvement allows an increase in the number of bits transmitted per symbol period while maintaining the same bandwidth, transmit power and bit error ratio, thus improving the capacity of the wireless link. It can also be used to extend distance over which a symbol can be transmitted, again while maintaining bandwidth, transmit power and bit error rate performance. This can improve the transmitter to receiver ratios, lowering site count and associated periodic costs.
Spatial multiplexing maximizes the link capacity that is sent over a given bandwidth by transmitting a different symbol on each antenna during each symbol period. Thus the number of symbols transmitted per symbol period is equal to the number of transmit antennas. For spatial multiplexing to work, the number of receive antennas must be greater than or equal to the number of transmit antennas. The space-time code in a spatial multiplexing scheme is inherent in the multiplexing function.
The predominant encoding schemes associated with spatial multiplexing break into two types: horizontal encoding and vertical encoding. In horizontal encoding, the bit stream to be transmitted is demultiplexed into T separate data streams. Each of these data streams is then temporally encoded, interleaved and converted to transmission symbols, with different modulation schemes allowed on each transmit channel. In contrast, in vertical encoding, the bit stream to be transmitted is encoded using a space-time block code and then converted into transmission symbols. The transmission symbols are then demultiplexed into T bit streams and transmitted.
Vertical encoding offers improved diversity gain over horizontal encoding because each data bit can be spread across all of the transmit antennas. However horizontal encoding accrues an advantage in receiver complexity in that the individual data streams are decoded separately, typically using a relatively simple linear receiver, such as the Zero Forcing receiver or Minimum Mean Squared Error receiver. Vertical encoding, on the other hand requires joint decoding at the receiver, which significantly increases receiver complexity.
A number of prototype architectures have been developed supporting MIMO technology, including specific architectures supporting commercial cellular and mobile networking communications.
On the receive side, this subsystem receives the digitized RF bands, extracts the channels of interest from these bands in the channelizer. Corresponding channels from each antenna are then forwarded to a common channel-processing engine for space-time processing, demodulation and temporal decoding.
This process is reversed on the transmit side, with payload data being temporally encoded, modulated and space time coded in the channel processor, and channels inserted into the output signals as appropriate by the various channelizers for retransmission. Channelization processing in these prototypes is typically performed using a Field Programmable Gate Array (FPGA) or Digital Down Converter Application Specific Standard Product (ASSP). Channel processing is performed using a combination of FPGAs, Digital Signal Processors (DSP) and General Purpose Processors (GPP).
Ultimately, the technical benefit accrued through the use of MIMO technology offers the wireless service provider the ability to increase revenue through exploitation of the enhanced capacity available per user channel, and save money through potential reductions in capital expenditures.
Massive MIMO ( Multiple Input Multiple Output ) is the new wireless access technology in 5G, in both sub-6 GHz and mmWave bands. The main idea is to use multiple antennas at a transmitter and receiver to improve the performance of wireless communication systems. Massive MIMO ( or very large ), is an extension of MIMO and uses more than 100 antennas.
The key concept is to equip base stations with arrays of many antennas, which are used to serve many terminals simultaneously, in the same time-frequency resource. The more antennas the transmitter/receiver is equipped with, the more the possible signal paths and the better performance in terms of throughput ( data rate), spectrum efficiency and link reliability.
A massive MIMO technique can increase 10 times or more channel capacity and improve 100 times or more energy efficiency. MIMO technique is well matched at a high-frequency technique such as millimeter wave (mmWAVE) techniques. Moving from MIMO to massive MIMO, according to IEEE, involves making “a clean break with current practice through the use of a large excess of service antennas over active terminals and time-division duplex operation. Extra antennas help by focusing energy into ever smaller regions of space to bring huge improvements in throughput and radiated energy efficiency.” The group calls out other benefits including cheaper parts, lower latency, “simplification of the MAC layer, and robustness against intentional jamming.”
The time for Massive MIMO has come at this moment for two reasons: First, conventional technology has proven unable to deliver the spectral efficiencies that 5G applications are calling for. Second, the confidence in the exceptional value of the technology has spread rapidly since impressive real-life prototypes showed record spectral efficiencies, and the robust operation with low-complexity RF and baseband circuits has been substantiated
Massive MIMO can offer enhanced broadband services in the future, and more. 5G networks are expected to support a great variety of wireless services in areas ranging from infotainment to healthcare, smart homes and cities, manufacturing, and many others. Massive MIMO technology can be tailored to support a massive number of Massive Machine Type Communication (MTC) devices. Also, it is an excellent candidate to realize Ultra Reliable Communication as it can establish very robust physical links. This method’s ability to multiply the capacity of the antenna links has made it an essential element of wireless standards including 802.11n (Wi-FI), 802.11ac (Wi-Fi), HSPA+, WiMAX and LTE.
Benefits of massive MIMO
Massive MIMO is a key enabler of 5G’s extremely fast data rates and promises to raise 5G’s potential to a new level. Massive MIMO can increase the capacity 10 times or more and simultaneously improve the radiated energy efficiency on the order of 100 times.
Excellent spectral efficiency, achieved by spatial multiplexing of many terminals in the same time-frequency resource. Efficient multiplexing requires channels to different terminals to be sufficiently different, which has been shown to hold, theoretically and experimentally, in diverse propagation environments. Specifically, it is known that Massive MIMO works as well in line-of-sight as in rich scattering.
Superior energy efficiency, by virtue of the array gain, permits a reduction of radiated power. The fundamental principle that makes the dramatic increase in energy efficiency possible is that with a large number of antennas, energy can be focused with extreme sharpness into small regions in space. By appropriately shaping the signals sent out by the antennas, the base station can make sure that all wavefronts collectively emitted by all antennas add up constructively at the locations
of the intended terminals, but destructively (randomly) almost everywhere else.
The primary benefits of massive MIMO to the network and end users can be summed up as:
Increased Network Capacity – Network Capacity is defined as the total data volume that can be served to a user and the maximum number of users that can be served with certain level of expected service. Massive MIMO contributes to increased capacity first by enabling 5G NR deployment in the higher frequency range in Sub-6 GHz (e.g., 3.5 GHz); and second by employing MU-MIMO where multiple users are served with the same time and frequency resources.
Improved Coverage – With massive MIMO, users enjoy a more uniform experience across the network, even at the cell’s edge – so users can expect high data rate service almost everywhere. Moreover, 3D beamforming enables dynamic coverage required for moving users (e.g., users traveling in cars or connected cars) and adjusts the coverage to suit user location, even in locations that have relatively weak network coverage.
User experience – Ultimately, the above two benefits result in a better overall user experience — users can transfer large data files or download movies, or use data-hungry apps on the go, wherever life takes them.
MIMO and massive MIMO technology
MIMO systems require a combination of antenna expansion and complex algorithms. MIMO algorithms come into play to control how data maps into antennas and where to focus energy in space. Both network and mobile devices need to have tight coordination among each other to make MIMO work.
Massive MIMO can be built with inexpensive, low-power components. Massive MIMO is a game changing technology with regard to theory, systems, and implementation. With massive MIMO, expensive ultra-linear 50 W amplifiers used in conventional systems are replaced by hundreds of low-cost amplifiers with output power in the milli-Watt range. The contrast to classical array designs, which use few antennas fed from high-power amplifiers, is significant. Several expensive and bulky items, such as large coaxial cables, can be eliminated altogether.
However, Building hundreds of RF chains, up/down converters, analog-to-digital (A/D)-digital-to-analog (D/A) converters, and so forth, will require economy of scale in manufacturing comparable to what we have seen for mobile handsets.
Massive MIMO reduces the constraints on accuracy and linearity of each individual amplifier and RF chain. All that matters is their combined action. In a way, massive MIMO relies on the law of large numbers to make sure that noise, fading, and hardware imperfections average out when signals from a large number of antennas are combined in the air.
The performance of wireless communications systems is normally limited by fading. Fading can
render the received signal strength very small at certain times. This happens when the signal sent
from a base station travels through multiple paths before it reaches the terminal, and the
waves resulting from these multiple paths interfere destructively. It is this fading that makes it
hard to build low-latency wireless links. Massive MIMO relies on the law of large numbers and beamforming in order to avoid fading dips, so fading no longer limits latency.
The same property that makes massive MIMO resilient against fading also makes the technology extremely robust to failure of one or a few of the antenna unit(s).
The greater number of antennas in a Massive MIMO network will also make it far more resistant to interference and intentional jamming than current systems that only utilise a handful of antennas. Intentional jamming of civilian wireless systems is a growing concern and a serious cybersecurity threat that seems to be little known to the public. Simple jammers can be bought off the Internet for a few hundred dollars, and equipment that used to be military-grade can be put together using off-the-shelf software radiobased platforms for a few thousand dollars. Massive MIMO offers many excess degrees of freedom that can be used to cancel signals from intentional jammers.
Massive MIMO arrays generate vast amounts of baseband data that must be processed in real time. This processing will have to be simple, and simple means linear or nearly linear. Fundamentally, this is good in many cases. Much research needs be invested in the design of optimized algorithms and their implementation.
Spatial diversity and spatial multiplexing
Spatial diversity is one of the fundamental benefits of MIMO technology. In brief, diversity aims at improving the reliability of the system by sending the same data across different propagation, or spatial, paths. Spatial diversity evolves into a more complex concept, which is “spatial multiplexing.” Now, not only are the diverse experiences of the over-air-channel utilized for performance improvements, but multiple messages can be transmitted simultaneously without interfering with one another since they are separated in space.
By nature, this solution is very dynamic. With the continuous movement of the mobile user and changes in the surrounding environment, the mobile phone and the network require more advanced capabilities to continuously coordinate the link and manage the data transmission.
Beamforming – principle of operation
Beamforming is another key wireless technique that utilizes advanced antenna technologies on both mobile devices and networks’ base stations to focus a wireless signal in a specific direction, rather than broadcasting to a wide area. Think of the difference between using a flashlight — which kind of floods everyone in the room — versus a laser pointer, which can pinpoint and continuously track a given user. With the massive number of antenna elements in a massive MIMO system, beamforming becomes “3D Beamforming.” 3D Beamforming creates horizontal and vertical beams toward users, increasing data rates (and capacity) for all users — even those located in the top floors of high-rise buildings.
It should be noted, too, that Massive MIMO networks will utilise beamforming technology, enabling the targeted use of spectrum. Current mobile networks are rather dumb in the way they apportion a single pool of spectrum between all users in the vicinity, which results in a performance bottleneck in densely populated area. With Massive MIMO and beamforming such a process is handled far more smartly and efficiently, so data speeds and latency will be far more uniform across the network. Terms beamforming and mMIMO are sometimes used interchangeably. One way to put it is that beamforming is used in mMIMO, or beamforming is a subset of mMIMO.
In general, beamforming uses multiple antennas to control the direction of a wave-front by appropriately weighting the magnitude and phase of individual antenna signals in an array of multiple antennas. That is, the same signal is sent from multiple antennas that have sufficient space between them (at least ½ wavelength). In any given location, the receiver will thus receive multiple copies of the same signal. Depending on the location of the receiver, the signals may be in opposite phases, destructively averaging each other out, or constructively sum up if the different copies are in the same phase, or anything in between.
The signal phases of individual antenna signals are adjusted in RF domain. Analog beamforming impacts the radiation pattern and gain of the antenna array, thus improving coverage. Unlike in digital beamforming, only one beam per set of antenna elements can be formed. The antenna gain boost provided by the analog beamforming overcomes partly the impact of high pathloss in mmWave. Therefore analog beamforming is considered mandatory for the mmWave frequency range 5G NR.
Digital beamforming (aka. Baseband beamforming, aka precoding)
The signal is pre-coded (amplitude and phase modifications) in baseband processing before RF transmission. Multiple beams (one per each user) can be formed simultaneously from the same set of antenna elements. In the context of LTE/5G, MU-MIMO equals to digital beamforming. Multiple TRX chains, one per each simultaneous MU-MIMO user, are needed in the base station. Digital beamforming (MU-MIMO) is used in LTE Advanced Pro (transmission modes 7,8, and 9) and in 5G NR. Digital beamforming improves the cell capacity as the same PRBs (frequency/time resources) can be used to transmit data simultaneously for multiple users.
Hybrid beamforming combines the analog beamforming and digital beamforming. It is expected that mm-wave gNB (5G base station) implementations will use some form of hybrid beamforming. One approach is to use analog beamforming for coarse beamforming, and inside the analog beam use a digital beamforming scheme as appropriate, either MU-MIMO or SU-MIMO.
The most commonly seen definition is that mMIMO is a system where the number of antennas exceeds the number of users. In practice, massive means there are 32 or more logical antenna ports in the base station It is expected that NEMs will start with a maximum of 64 logical antenna ports in 5G.
In antenna array of 50 omni elements, with ½ wavelength spacing in between the antenna elements is used. The 50 elements transmit 4 distinct streams of data via 4 logical antenna ports, one stream for each UE. All four streams are transmitted using the same physical resource blocks, i.e. the same time/frequency resources. The data streams do not interfere between each other because each of them has a distinct radiation pattern, where the signal strength in the direction of the target UE is optimized, and in the directions of the other UEs (victim UEs) the signal strength is minimized.
In MU-MIMO/mMIMO, the base station applies distinct precoding for the data stream of each UE where the location of the UE, as well as the location of all the other UEs, are taken into account to optimize the signal for target UE and at the same time minimize interference to the other UEs. To do this, the base station needs to know how the downlink radio channel looks like for each of the UEs.
ML and AI for Beamforming
5G, deployed using mm-wave, has beam-based cell coverage unlike 4G which has sector-based coverage. A machine learned algorithm can assist the 5G cell site to compute a set of candidate beams, originating either from the serving or its neighboring cell site. An ideal set is the set that contains fewer beams and has a high probability of containing the best beam. The best beam is the beam with highest signal strength a.k.a. RSRP. The more activated beams present, the higher the probability of finding the best beam; although the higher number of activated beams increases the system resource consumption.
The weights for antenna elements for a massive MIMO 5G cell site are critical for maximizing the beamforming effect. ML and AI can be used to:
- Identify dynamic change and forecast the user distribution by analyzing historical data
- Dynamically optimize the weights of antenna elements using the historical data
- Perform adaptive optimization of weights for specific use cases with unique user-distribution
- Improve the coverage in a multi-cell scenario considering the inter-site interference between multiple 5G massive MIMO cell sites
Massive MIMO breakthroughs
Massive MIMO technology is already live commercially in China and Japan within a 4G LTE context. The latter country’s Softbank network deployed the first ever commercial Massive MIMO network towards the end of 2016.
In early September 2017, Ericsson announced the launch of a new FDD (Frequency Division Duplexing) radio with support for 5G and Massive MIMO. It claimed that this would bridge the gap between 4G and 5G, boosting the capacity of existing 4G LTE while forming the foundation for 5G.
Early in 2018, Nokia took a step towards refining Massive MIMO antenna technology itself with the production of its ReefShark chipset. By being smaller, lighter and more power efficient than its predecessors, Nokia has claimed that ReefShark reduces the massive MIMO antenna size by half and cuts the energy consumption in baseband units by 64%.
Korean tech giant Samsung has also been busy with Massive MIMO technology. At its headquarters in Suwon, Korea, the company has created a so-called ‘5G City’ to provide some insight into a what life might be like when 5G is rolled out.
One major element of this 5G City was a so-called ’5G Stadium,’ which specifically demonstrated how massive MIMO technology can enable crowds of people to simultaneously stream HD video without any delays or interruptions.
Testing the performance of multiple parallel signals has actually been quite a challenge for the telecoms industry. Cobham Wireless addressed this in March 2018 with a multi-beam Massive MIMO performance test solution it developed in conjunction with the China Mobile Research Institute. It enables testing in a virtualised environment that simulates real-world conditions, which was a step forward from the limited-scale lab tests that had preceded it.At MWC 2019 in Barcelona, Ericsson won the award for Best Mobile Network Infrastructure with its 5G high-band Massive MIMO. This is the first ever commercial enhanced mobile broadband solution operating on millimetre wave, aka the higher frequency bands that will come to define 5G.
Samsung Electronics, has announced the development of breakthrough 5G-ready case-integrated antenna, which incorporates dozens of antenna elements in a module that is less than 1mm thick – a critical step towards engineering both compact small cell base stations and user devices. The new technologies are intended to be applied to both 5G base stations and end-user devices using 28GHz millimeter wave (mmWave) spectrum. mmWave frequencies are widely expected to be one of the primary enablers of next-generation networks.
Russian Scientists Improve Material for 5G Communication Antennas, reported in June 2020
Ceramics with improved properties are tested by scientists of South Ural State University. The material can be used in duplexers of MIMO antennas for 5G stations, improving their receiving and transmitting properties. Massive Multiple-Input Multiple-Output (MIMO) antenna has a duplexer-a frequency-separation filter. According to experts, ceramic filters will be preferred. Compared to metal hollow filters, which are still used, for example, by ZTE and Nokia, ceramic devices are smaller in size and weight but have a high Q factor.
The material from which the filters are made performs a significant role in their effectiveness. Therefore, SUSU scientists have developed their own version of heat-resistant ceramics. It was based on complex dielectric oxides—one of the most standard materials to date. “The relative simplicity of the synthesis, the possibility of varying microwave parameters due to changes in the chemical composition in a vast range of concentrations, along with excellent chemical stability and the required dielectric characteristics make it possible to consider complex oxides as the most promising area for antenna resonators,” explained a senior research fellow at the REC crystal growth laboratory “Nanotechnology” SUSU Sergey Trukhanov.
SUSU has developed a method for the synthesis of complex oxides by means of solid-phase reactions—mixing solid materials. The researchers added V2O5 (vanadium oxide) and CuO-B2O3 (copper metabolite) as an inhibitor, thereby reducing the sintering temperature of ceramics. In the samples, the grain size distribution was imparted homogeneity, that is, provided uniformity. The grain density was equally higher.
“The SUSU laboratory certified the structural and electrodynamic characteristics of samples in wide frequency and temperature ranges. A correlation was established between the use of various oxide additives and the physical characteristics of the obtained materials. Our colleagues from China have studied the antenna characteristics of synthesized materials. Data on temperature stability and ultra-low losses in the studied material were obtained. A prototype antenna made of a dielectric resonator made of our ceramic has demonstrated good performance,” said Sergey Trukhanov.
Huawei hails Massive MIMO Breakthrough With Release of MetaAAU, reported in Nov 2021
New services also require higher downlink and uplink capacity. According to industry estimates, high-definition live broadcast video and 2x/3x playback speed require much greater downlink and uplink capacity than traditional video services.
Cloud based gaming, which is growing in popularity, also requires greater uplink throughput than normal smartphone-based mobile gaming. If attractive 5G user-experiences are to be guaranteed, appropriate quality of service is needed at the cell edge and indoors.
MetaAAU, developed by Huawei, incorporates ELAA (extreme large antenna array) technology supporting 384 antenna elements. It’s double the number of a traditional AAU. “By introducing 384 antennas in the AAU, coverage can be improved by 3dB on both the downlink and the uplink, and the user experience can also be improved by 30%,” said Chaobin, “Energy savings of 30% can also be achieved.”
If traditional materials found in antenna dipoles were applied to ELAA, for example, the weight would drastically increase, making it more difficult and expensive to install on cell sites.
Moreover, without miniaturized filters, ELAA dimensions necessarily become much bulkier compared with traditional massive MIMO antenna. Cell-site space is already constrained and operators don’t want to go through the lengthy process of gaining permission to occupy more tower space, which, in turn, increases maintenance costs.
Another challenge is that antenna elements in a traditional RF feeding network architecture are normally connected by cables, which are an inefficient way to transfer signals. If the antenna array doubles to 384 elements, the length of cable – along with the extent of inefficiencies – increases.
Through a series of hardware innovations, however, MetaAAU makes the transition to ELAA feasible and attractive. For one thing, MetaAAU is around the same weight as the original 64T64R massive MIMO AAU. Adoption of Huawei’s compact wave filter also means MetaAAU dimensions do not require more space. To address hardware energy inefficiencies, Huawei has adopted SDIF (signal direct injection feeding) technology. SDIF replaces cables with a more energy-efficient metal-type structure.
Aside from hardware innovation, MetaAAU introduces an adaptive high-resolution beamforming algorithm, dubbed AHR (Adaptive High Resolution) Turbo. It has various features, which, when combined, not only reduces wasted radiation energy but also cuts down on ‘noise’ that can degrade network performance.
Among the benefits of AHR Turbo is that it enables MetaAAU to generate extremely narrow beams that can precisely latch onto user equipment, as well as boost air-interface efficiencies by allowing beams to dynamically adapt to radio channel changes.
MetaAAU has already been demonstrated on various commercial sites in China and has performed impressively in field tests. It provides 3dB better coverage and 30% better user experience than traditional 64T64R AAU, and when compared with the 32T32R AAU, MetaAAU improves coverage by 6dB and user experience by up to 60%.
Huawei has also demonstrated that transmission power can be reduced from 320W to 160W without decreasing coverage. What’s more, based on a 24-hour power consumption test on commercial networks, MetaAAU has shown that it can achieve a 30% energy saving compared with traditional massive MIMO AAU when serving the same coverage area.
6G era’s enormous capacity demands will require new spectrum and extreme massive MIMO
Traffic on mobile networks has increased by several orders of magnitude since inception of mobile data in 3G, and demand will only continue increasing in the future. To offer some perspective, a 35% year-over-year increase in demand would necessitate a 2000% increase in capacity over 10 years. Hence 6G must be designed to provide, at minimum, 20 times more wide-area capacity than 5G.
Every generation of mobility has increased capacity by expanding carrier bandwidth – in essence creating a broader swathe of airwaves over which to transmit information. The move from 3G to 4G grew carrier size from 5 MHz to 20 MHz, while the transition from 4G to 5G saw carrier bandwidth grow from 20 MHz to 100 MHz. Researchers at Bell labs estimate that with 6G spectral bandwidths to increase once again, reaching 400 MHz, greatly increasing the baseline capacity of a single cell.
For instance, regulators are looking at the 470-694 MHz band as a means for providing broad coverage in rural and remote regions. The low frequencies in this band mean signals propagate much further, extending the network’s reach. We may also see sub-THz bands beyond 90 GHz come into use, which could supply extremely high peak data rates for the most bandwidth-intensive applications as well as connect highly dense sensing networks.
New spectrum alone, however, isn’t enough. By expanding carrier bandwidth from 100 MHz to 400 MHz gives us a 4X increase in capacity at most, well short of the 20X demand of the 6G era. We will need to utilize and re-use that spectrum in new ways.
The last several generations of mobility have seen spectral efficiency of wide area cells improve substantially mainly through the application of more sophisticated multiple-input multiple output (MIMO) techniques. In short, we’ve added more antenna elements to each successive generation: 4G uses 2x2MIMO and 4x4MIMO while 5G benefits from massive MIMO using around 200 antenna elements and up to 64 transceivers. 6G may support on the order of 1024 antenna elements in the new mid-bands.
Major steps in technology evolution are required to make these high-performance arrays possible. We will need new scalable, low-power radio-frequency and digital front ends, more sophisticated beamforming algorithms, and high-capacity fronthaul and baseband processing.
References and resources also include: