The Antenna Evolution Story: From SISO for 2G to Extreme Massive MIMO for 6G

1.??Background

Wireless communications and especially mobile communication systems are growing at an incredible pace. New concepts like Machine-to-Machine communications (M2M) and Internet of Things (IoT) in a fully connected wireless network, together with the dissemination of services like video on demand or 3D video and augmented reality consumed by mobile users, are contributing to the exponential increase of data traffic in wireless cellular networks. In fact, in the next few years, an increase of 1000 times in data traffic in wireless mobile networks is expected to respond to this global demand. 5G-Advanced and 6G networks aim to provide advanced capabilities from new services, extreme connectivity, and security to RF sensing. The future radio networks should also provide more capacity at substantially lower cost per bit and lower energy consumption.

To support such demand, the cellular network must dramatically increase its capacity, and using more bandwidth or increasing the network cell density will not give a viable answer to it. Another problem lies in the energy needed to support the high bit rates and the Quality of Service (QoS) of these broadband communication links. Increasing the bandwidth has its drawbacks, in terms of reducing the Signal-to-Noise Ratio (SNR) by Hertz for the same transmitted power, which justifies the focus of the current works on techniques that improve the spectral efficiency.

The radio communication systems must meet a growing number of users and increasing demand for new applications, new traffic types, and data services. As an example, machine-to-machine communications will support concepts such as the smart grid, smart homes and cities, and e-health, and these applications have very diverse communications requirements, needed for a unified wireless technology to work seamlessly.

The industry is in search of a solution that can offer high-speed transmission with a minimum quality of service guarantee. Improvement in throughput and the spectral efficiency aspects is crucial. A well-known way to increase the spectral efficiency is using multiple antennas at the transceivers. Energy efficiency is another aspect whose importance has been increasing along with the demand for wireless systems. As these systems expand in different domains (power, antennas, terminals, base stations), power consumption would have grown in an unacceptable manner by using the classical techniques. New architectures and approaches are being developed with the energy efficiency goal.

2.??SISO

SISO stands for “Single Input, Single Output” refers to a wireless communications system in which one?antenna?is used at the source (transmitter) and one antenna is used at the destination (receiver). SISO is the simplest antenna technology.?The advantage of a SISO system is its simplicity. SISO requires no processing in terms of the various forms of diversity that may be used.?

In some environments, SISO systems are vulnerable to problems caused by multipath effects. When an electromagnetic field is met with obstructions such as hills, canyons, buildings, and utility wires, the wavefronts are scattered, and thus they take many paths to reach the destination. The late arrival of scattered portions of the signal causes problems such as fading, cut-out (cliff effect), and intermittent reception (picket fencing). In a digital communications system, it can cause a reduction in data speed and an increase in the number of errors.

SISO is?typically used in radio, satellite, GSM, and CDMA systems. The antennas perform the activity of both transmitting and receiving the signal to establish the datalink. A typical MANET SISO [Mobile Adhoc NETwork] datalink will achieve a throughput in the region of about 20Mbps. The legacy Wave Relay MPU4 radio is an example of a SISO radio.

3.??MIMO

MIMO refers to “Multiple Input, Multiple Output” basically refers to system having more than one antenna elements used either to increase system capacity, throughput, or coverage. Beamforming techniques are used to concentrate radiated energy towards target UE which reduces interference to other UE’s and thereby improves the coverage.

MIMO technology greatly improves the data transmission efficiency of a single device without occupying extra spectrum resources. MIMO has greatly contributed to the surge of?Wi-Fi?transmission rates – for example, evolving from 54 Mbit/s (802.11g) to 300 Mbit/s or even 600 Mbit/s (802.11n).

There are two major types of MIMO with respect how the BS (Base Station) transmission is utilized by the mobile or fixed users. They are SU-MIMO and MU-MIMO. Both the types are used in the downlink direction i.e., from Base Station or eNB or Access point towards users.

4.??MIMO Antenna Beamforming

MIMO Antenna Beamforming technology focuses radio signals towards wireless stations (STAs) in specific directions, thereby comprehensively improving the received signal strength indicators (RSSIs) of STAs and also increasing STA throughput.?Beamforming techniques can be used with any antenna system - not just on MIMO systems. They are used to create a certain required antenna directive pattern to give the required performance under the given conditions. Smart antennas are normally used - these are antennas that can be controlled automatically according to the required performance and the prevailing conditions.

Wi-Fi?standards have been dedicated to increasing transmission rates. Since Wi-Fi 4 (802.11n) introduced?multiple-input multiple-output?(MIMO) and beamforming technologies, the maximum transmission rate has surged to hundreds of megabits per second and even higher.

Signal beams are like light beams. The shape of a light beam produced by a flashlight is fixed. If another flashlight produces a light beam in the same direction, the two light beams are superimposed, thereby increasing the beam brightness, and changing the beam shape. If more flashlights are used, the brightness and shape of the superposed beam both continue to change. In the case of multiple flashlights, turning on/off the flashlights or adjusting the light intensity also affects the beam shape.

Likewise, in wireless communication, an antenna emits radio beams like a flashlight. With multiple antennas, the radio beam shape can be adjusted by controlling radio signals transmitted by each antenna.

5.??SU – MIMO

SU-MIMO stands for “Single User – Multiple Input, Multiple Output” was an optional technology that came along with 802.11n standards.?Single User MIMO operates multiple streams of data must be sent or received between just one device at a time.?This technology requires both the transmitting and receiving Wi-Fi radios support the MIMO technology, along with having multiple antennas.

In SU-MIMO, all the streams of antenna arrays are focused on single user. Hence it is referred as Single User MIMO. It splits the available SINR between different multiple data layers towards target UE simultaneously where each layer is separately beamformed. This increases peak user throughput and system capacity. Here cell communicates with single user. Advantages of SU-MIMO technology is no interference.

6.??MU – MIMO

MU-MIMO stands for “Multiple User – Multiple Input, Multiple Output” wherein multiple streams are focused on multi users. Moreover, each of these streams provide radiated energy to more than one user. It shares available SNR between multiple data layers towards multiple UEs simultaneously where each layer is separately beamformed. This increases system capacity and user perceived throughput. Here cell communicates with multi users.

While MU-MIMO only became available on wave 2 of 802.11ac standards.?This technology enables Wi-Fi to simultaneously transmit those multiple streams to different Wi-Fi devices, instead of just one single device when compared to the older version (SU-MIMO).?Another benefit to 'MU' is the?Wi-Fi devices receiving one of the MIMO data streams doesn't have to have multiple antennas therefore even devices with single antenna will support this. However, the receiving Wi-Fi devices must support the MU-MIMO technology.

MU-MIMO works with?beamforming?technology to implement simultaneous communication with multiple terminals. An access point measures the characteristics of channels from each antenna to each terminal. Then, based on the channel characteristics, the access point performs precoding calculation on the data to be transmitted, and transmits the pre-coded signals on each antenna. In this manner, when data from all antennas reaches terminals, each terminal receives only data destined for themselves. This is similar to forming directional beams to each terminal.

MU-MIMO can have a positive impact on wireless networks in scenarios where users are densely distributed, a large amount of concurrent traffic occurs for multiple users, and terminals are relatively fixed at specific locations, for example, offices, conference centres, and electronic classrooms (e-classrooms).

MU-MIMO can improve the throughput of a wireless network by two to three times compared with a wireless network using SU-MIMO. MU-MIMO enables more antennas on an AP to play their roles, thereby providing more space resources and delivering higher throughput.

MU-MIMO allows multiple terminals to transmit data concurrently, which improves data transmission efficiency on a wireless network while reducing the waiting time of terminals in terms of time sequence. Therefore, MU-MIMO can better meet the requirements of video, audio, and other applications that require high bandwidth and low latency.

The overall efficiency improvement brought by MU-MIMO enables more idle time or capacity on the network to serve legacy Wi-Fi terminals (that support only SU-MIMO). This improves the application experience for legacy Wi-Fi terminals.

7.??CMIMO

CMIMO or Cooperative MIMO is a form of cooperative communications. CMIMO emulates the functionality of multi-antenna systems by grouping wireless devices to operate as virtual multi-antenna nodes. Its main objectives are to boost network throughput, conserve energy, and improve network coverage. In this article, we discuss recent applications of CMIMO in contemporary wireless networks, including wireless sensor, mobile ad hoc, wireless LAN, cognitive, and cellular networks.?

In cooperative communications, a group of nodes that lie within a certain proximity can cooperate in sending (receiving) a signal to (from) another group of nodes. Cooperative MIMO (CMIMO), sometimes referred to as distributed, virtual, or networked MIMO, is one type of cooperative communications, whereby several nodes, each equipped with one or more antennas, cooperate to emulate a multi-antenna node, also known as a virtual antenna array (VAA). CMIMO allows small devices to harvest MIMO gains, and moreover, offers numerous advantages that are beyond what is typically expected from a real multi-antenna system. For instance, unlike real MIMO systems, CMIMO can flexibly select its distributed antennas to avoid having a low-rank channel gain matrix so that the spatial multiplexing gain can be better harvested (with high-rank channels).

CMIMO has been shown to improve the network lifetime, throughput, and reduce the communication delay. Network lifetime is a critical performance metric in energy-constrained systems such as wireless sensor networks (WSNs). Thanks to CMIMO’s higher energy efficiency, the lifetime of a WSN can be prolonged by several times, compared with that of a single-input single output (SISO) approach. The throughput and communication delay of mobile ad hoc networks (MANETs) can also be dramatically improved by exploiting the higher transmission range, higher spectrum efficiency, and better interference management capability of CMIMO.

8.??Macrodiversity MIMO

Macrodiversity MIMO systems deals with antenna elements wherein the transmitter and receiver are widely separated. A communication system where antenna elements at both source and receiver are geographically separated is described as a macrodiversity communication system. For these macrodiversity systems, every link may have a different average signal to noise ratio (SNR) since the sources and the receive antennas are all in different locations. This variation in average SNR across the links makes the performance analysis of such systems more complex. For this reason, most of the results currently available are based on simulation. However, the value of analytical results can be immense for efficient computation and optimized operation.

9.??Massive MIMO

Massive MIMO systems can be defined as those that use tens or hundreds of antennas in the communication terminals. Traditional MIMO systems may have two or four, some may even have eight antennas, but this has been the limit on early systems that have adopted MIMO. Massive MIMO is also termed as FD MIMO or Full Dimensional MIMO.

There are many advantages to using large MIMO technology. Using more antennas in a MIMO system creates more degrees of freedom in the spatial domain and therefore this enables greater improvement in performance to be achieved. The increase in the number of antennas allows for a greater number of paths to be used and hence a much greater level of data to be transferred within a given time. One of the basic advantages of the use of MIMO systems is that it can be used to improve the signal to noise ratio of the overall system.?

Massive MIMO is the key technology of 5G, and network operators are actively deploying 5G to provide superior experience to both consumers and the enterprise. 5G revenue is dominated by massive MIMO and Asia Pacific generates 60% of the total revenue in the world market. According to a global technology intelligence firm,?by 2027, there will be 41 million mMIMO deployments worldwide, reaching revenue of US$43 billion, representing 35% of the total revenue in outdoor infrastructure. Furthermore, deployment will be led by Asia Pacific due to the large adoption in countries like China, Japan, and South Korea.

Network operators around the world are actively deploying mMIMO and there is regional difference in mMIMO deployments. 5G in China initially started with dense urban areas so they were mainly deploying mMIMO in 64T64R configuration. Since the 2021 move toward the suburbs, the 32T32R mMIMO configuration became preferred. The semiconductor shortage may be another contributing factor. Europe mainly deploys mMIMO in 32T32R configuration as they prioritize simple site migration and the high-power consumption associated with 64T64R. Due to space and weight constraints, South Korea, and Japan favour 32T32R mMIMO. The U.S. operators are aggressively deploying 64T64R mMIMO as 5G in the United States still focuses on dense urban areas. 64T64R is the primary use in the Middle East region and this configuration is expected to the adopted in Latin America as well.

The fast-growing power consumption of 5G is burdening network operators with high energy costs and they are demanding equipment that is more energy efficient. PAs (Power Amplifiers) represent up to 60% of the mMIMO power consumption with high traffic load.

Massive MIMO technology provides dramatic advancements in throughput and spectral efficiency which enable performance improvements for superior 5G consumer experiences. The emergence of Massive MIMO technologies has greatly helped the development of 5G networks and greatly improved user experience.

10.??Extreme Massive MIMO

Extreme Massive MIMO refers to the technology that uses very large antenna arrays. The increasingly demanding objectives for 6th Generation (6G) communications have spurred recent research activities on next generation wireless base stations and access points that will transmit and receive using massive numbers of antennas. A promising technology for realizing such massive arrays in a dynamically controllable and scalable manner with reduced cost and power consumption utilizes surfaces of radiating metamaterial elements, known as metasurfaces. To date, metasurfaces are mainly considered in the context of wireless communications as passive reflecting devices, aiding conventional transceivers in shaping the propagation environment.

Over the last few years, metamaterials have emerged as a powerful technology with a broad range of applications, including wireless communications. Metamaterials comprise a class of artificial materials whose physical properties, and particularly their permittivity and permeability, can be engineered to exhibit various desired characteristics. When deployed in planar structures (a.k.a. metasurfaces), their effective parameters can be tailored to realize a desired transformation on the transmitted, received, or impinging EM waves. Such structures have been lately envisioned as a revolutionary means to transform any naturally passive wireless communication environment to an active one. Their extremely small hardware footprint enables their cost-effective embedding in various 3D components of the environment (e.g., building facades and room walls/ceilings).

Dynamic Metasurface Antennas (DMAs) have been recently proposed as an efficient realization of massive antenna arrays for wireless communications. They provide beam tailoring capabilities and facilitate processing of the transmitted and received signals in the analog domain in a flexible and dynamically configurable manner using simplified transceiver hardware. In addition, DMA-based architectures require much less power and cost compared with conventional antenna arrays (i.e., those based on patch arrays and phase shifters) eliminating the need for complicated corporate feed and / or active phase shifters. DMAs may comprise a large number of tuneable metamaterial antenna elements that can be packed in small physical areas for a wide range of operating frequencies. This feature makes them an appealing technology for the extreme mMIMO transceivers of 6G wireless networks. In contrast to passive metasurfaces that have received extensive attention recently, the potential and capabilities of metasurfaces as active mMIMO antenna arrays, as well as their associated challenges are yet to be fully explored. Massive investment is being done today to fully understand the potential of DMA and investigate the possibility of using this technology for 6G communications.

11.??Future

Today, the amount of knowledge in the world is doubling every 12 hours, and the real-time data from which new knowledge is extracted is growing even faster. All that data and knowledge is being transmitted through communication networks. As a consequence, demands on our networks have grown astronomically, and nowhere have those pressures more apparent than on mobile networks. Traffic on mobile networks has increased by several orders of magnitude since inception of mobile data in 3G, and this 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.

Future wide area mobile networks can deliver substantially more capacity than current 5G networks. When the deployment is largely limited to the use of existing macro sites, the main solution is new mid-band spectrum at 7–20 GHz, an extreme massive MIMO antenna and a beamforming-optimized air interface. As an example, the evolution could provide 20 times more capacity while mostly using the existing site grid in urban areas. Such a boost in the network capacity requires global collaboration to make the spectrum blocks available. We will need, as well, innovations in the underlying technology to make extreme massive MIMO feasible, including RF architecture and front-end technologies, AI-optimized beamforming, baseband processing capabilities and improved power efficiency.

Few other Massive MIMO related research directions include Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.

12.??References?