research topics on 5g

A comprehensive survey 5G wireless communication systems: open issues, research challenges, channel estimation, multi carrier modulation and 5G applications

Published: 12 June 2021
Volume 80 , pages 28789–28827, ( 2021 )

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Nilofer Shaik 1 , 2 &
Praveen Kumar Malik ORCID: orcid.org/0000-0003-3433-8248 2

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The fifth generation (5G) organize is required to help essentially enormous measure of versatile information traffic and immense number of remote associations. To accomplish better spectrum, energy-efficiency, as a nature of quality of service (QoS) in terms of delay, security and reliability is a requirement for several wireless connectivity. Massive Multiple-input Multiple-output (mMIMO) is a rising innovation for the 5G wireless communication systems. It can possibly give high spectral efficiency, improving link reliability and suit huge number of clients likewise focusing on the efficacy, accuracy and estimation of channel many channel estimations (CE) techniques are developed. Much of the time, the accentuation of most proposed CE schemes is to improve the CE performance and complexity for ensuring improved system throughput and quality signal reception. This article reviews mainly about 5G wireless communication systems that requires efficient channel estimation technique with an efficient candidate wave form. The article also reviews the architecture and design issues that are mere requirement for 5G wireless communication systems.

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Shaik, N., Malik, P.K. A comprehensive survey 5G wireless communication systems: open issues, research challenges, channel estimation, multi carrier modulation and 5G applications. Multimed Tools Appl 80 , 28789–28827 (2021). https://doi.org/10.1007/s11042-021-11128-z

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Published : 12 June 2021

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DOI : https://doi.org/10.1007/s11042-021-11128-z

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As 5G rolls out across the United States, wireless customers may be looking forward to faster downloads and seamless streaming—but the fifth-generation wireless network could impact much more than our smartphones and tablets. 5G’s increased speed and capacity, as well as new features, will open a world of possibilities for scientists working on data-driven projects, or in remote or extreme environments.

Through its 5G Initiative, the U.S. Department of Energy’s Office of Science is funding projects at the DOE national laboratories to demonstrate how advanced wireless will benefit fundamental science research. Based on insight from scientists at the national laboratories during a 2020 workshop called “5G-Enabled Energy Innovation,” the Office of Science established the initiative and awarded $6 million in 2021.

5G networks outperform 4G in data movement, including bandwidth (data per second), latency (or lag), and density (the number of supported devices). The network can transmit up to 100 times more data with only millisecond delays, on par with many wired networks. 5G can also support about 100 times more devices within a given area because the network is built from many smaller, denser, and modular cells.

These updates mean 5G can support advances in data-rich fields such as artificial intelligence, automation, and quantum information science that 4G cannot. In particular, scientists are interested in using these capabilities to drive wireless devices in the field and in the laboratory. Wireless devices that collect data, such as sensors or drones, are at the “edge” of the digital continuum—or a series of connected electronics, from small devices to large data centers. Devices at the edge are typically programmed to perform a specific task, either sending information to or receiving instruction from a central computer. However, 5G could power more computation at the edge, making these frontline devices smarter and more versatile.

For example, 5G may support a flood of interacting, wireless devices with artificial intelligence programs, such as autonomous vehicles navigating rush-hour traffic. In laboratories and industrial settings, robots could be untethered from wires and become more agile, allowing for increasingly sophisticated movements and tasks. At large science facilities like light sources and particle accelerators, individual experiments might be autonomously optimized in real time to solve a particular science problem, rather than reconfigured hours or days later after human analysis.

Also, 5G’s low latency may let us convert traditionally wired systems to wireless, such as industrial control systems that depend on a reliable, high-speed network for performance and safety. Wireless control systems could enhance how we monitor and optimize the flow of electricity on the power grid, or how we design large, state-of-the-art experiments.

A new feature of 5G could be exceptionally useful for science. Unlike previous wireless generations, 5G networks can be sliced to provide tailored services to different connected devices. One device on a science experiment’s network may need lower latency to control the operation of experiment parts, whereas another device may need higher bandwidth to collect and share data. Network slicing also reduces energy use by not wasting power on unused services.

Overall, energy use for 5G networks is expected to drop as much as 90 percent, which presents new possibilities for experiments that have long data collection times. For example, experiments to measure the effects of climate change often place sensors and other equipment in rainforests, arctic tundra, or other remote locations for years at a time. If these devices use less energy, they may never need new batteries or hands-on maintenance—cutting costs, time, and risk for scientists.

Other features of 5G, such as high bandwidth, may also allow field sensors to be further spread out. These distributed sensors could wirelessly coordinate with each other to collect higher quality data over larger surface areas. Further, 5G’s higher frequency range may enable connectivity in extreme environments, such as underground, space, or inside hot or radioactive environments like nuclear reactors. Access to extreme environments could lead to entirely new research opportunities.

Last, but not least, security and privacy for 5G networks for science will be essential. Part of the research funded by the Office of Science explores how we can protect information relayed through 5G networks. For systems like the power grid or autonomous vehicles, securing wireless communications is not just about information protection but also physical safety.

5G has the potential to impact all areas of science research and could revolutionize our nation’s science infrastructure. In addition to the possible applications described here, scientists will likely find other innovative ways to apply advanced wireless to research and discovery.

The Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information please visit www.energy.gov/science .

Katie Elyce Jones is a science writer for the Office of Science Office of Communications and Public Affairs, [email protected] .

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Next Generation Telecommunications - Advancements, Challenges, and Future Prospects

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The rapid evolution of telecommunication technologies (5G and beyond) has paved the way for a new era of connectivity and communication supporting emerging applications including eXtended Reality (XR), telesurgery, autonomous vehicles, tactile Internet etc. The Research Topic on Next Generation Telecommunications aims to bring together cutting-edge research and insights making transformative impact of next generation telecommunication technologies for communities, industry, and Government. The goal of this collection is to provide a platform for the promotion of research by highlighting recent developments, encouraging researchers to contribute new insights and findings, and offering a comprehensive overview of the current research, methodologies, and theoretical frameworks in next generation telecommunication. The Research Topic invites contributions that explore a wide range of topics including, but not limited to: - Architectures, protocols, and emerging applications for 5G and beyond - mmWave communications and THz bands - Integration of Multi-Access Edge Computing with next generation telecommunications to enhance network performance and efficiency - Advances in Software Defined Networking and Network Function Virtualisation for network automation and orchestration - AI and machine learning techniques to optimize network performance, operations, and resource allocation - Cybersecurity challenges related to combined sensing, communications, and computing in 5G mmWave and UWB radio spectrum - Energy efficiency and green communications The Editorial team is interested in receiving original research papers, review articles (comprehensive or systematic literature review), and conceptual or theoretical papers.

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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.

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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 .

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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.

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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.

Performance Index	4G	5G	6G
Peak Data Rate	100 Mbps	10 Gbps	Upto 10 Tbps
Latency	10 ms	1 ms	Upto 0.1 ms
Connection Density	0.1 million devices/km	1 million devices/km	10 million devices/km
Energy Efficiency	1×	100 × 4G	100 × 5G
Spectral Efficiency	1×	100 × 4G	100 × 5G
Available Spectrum	Upto 6 GHz	Upto 300 GHz	Upto 3 THz
Mobility	200 m/h	300 m/h	600 m/h
Artificial Intelligence	No	Partial	Fully

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 .

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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 .

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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.

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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 .

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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 ].

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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 ].

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Massive MIMO uplink and downlink.

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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.

	MIMO	Massive MIMO
Number of Antenna	≤8	≥16
Pilot Contamination	Low	High
Throughput	Low	High
Antenna Coupling	Low	High
Bit Error Rate	High	Low
Noise Resistance	Low	High
Diversity/Capacity Gain	Low	High
Energy Efficiency	Low	High
Cost	Low	High
Complexity	Low	High
Scalability	Low	High
Link Stability	Low	High
Antenna Correlation	Low	High

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 .

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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.

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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 .

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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 ].

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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.

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( 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.

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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.

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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 .

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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.

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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 ].

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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.

Feature	Massive MIMO System
Main aspect	Base station with hundreds of antennas
	Multiple users
	Low power antennas
Characteristics	Many more antennas than number of users
	Multiplexing gain
	Small low power antennas
	Very directive signals
	Little interference leakage
Technical Content	Number of antennas ≥ 16
	High channel capacity
	High throughput
	High antenna coupling
	Low BER
	High noise resistance
	High implementation cost
	High scalability
	High link stability
	High antenna correlation
Benefits	High spectral efficiency
	Array gain
	High energy efficiency
	High data rate
	User tracking
	Low power consumption
	Less fading
	Low latency
	More reliability
Challenges	Pilot contamination
	Channel estimation
	Precoding
	User scheduling
	Hardware impairments
	Energy efficiency
	Signal detection

Summary of Challenges and Mitigation Techniques in Massive MIMO System.

Challenges	Mitigation Techniques
Pilot Contamination	Pilot based Estimation [ , ], Subspace based Estimation [ ], Pilot Reuse [ ], Partial Sounding Resource [ ], Pilot Contamination Precoding [ ], Blind Pilot Decontamination [ , ], Pilot Decontamination [ ], Distributed Non-Orthogonal Pilot Design [ ].
Channel Estimation	Least Square [ ], MMSE [ , ], Improved MMSE [ , ], Blind Estimation [ , ], Compresses Sensing [ , ], MICED [ ], Untraind Deep Neural Network [ ], Compressed Sensing [ ], Convolutional Blind Denoising [ ], VAMP [ ], Deep Learning based Sparse Estimation [ ], CNN based Estimation [ ], Machine Learning based Estimate [ , ], Deep Learning based Estimation [ , ]
Precoding	DPP [ ], TH [ , ], VP [ ], MRC [ ], ZF [ , ], WF [ ], MMSE [ , ]
User Scheduling	ZF [ ], MMSE [ ], DPC [ ], RR [ ], PF [ ], Greedy [ ], Multi-user Grouping [ ], Gibbs Distribution Scheme [ ], Pilot Efficient Scheduling [ ], Machine Learning based Scheduling [ ]
Hardware Impairments	Digital Pre-Distortion [ , ], PAPR [ ],
Signal Detection	SD [ ], SIC [ ], ML [ ], ZF [ ], MMSE [ ], NSA [ ], Richardson [ ], SOR [ ], Jacobi [ ], Gauss Siedel [ ], Conjugate Gradient [ ], Least Square Regression Selection [ ], Huber ADMM [ ], AMP [ ] Compressed Sensing based Adaptive Scheme [ ], CNN [ ], Gauss Siedel Refinement [ ], SSL and SL based Detection [ , ], APRGS [ ]

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:

MIMO	Multiple-input multiple-output
IoT	Internet of things
M2M	Machine to machine
LTE	Long term evolution
LAN	Local area network
MAC	Media access control
FDMA	Frequency division multiple access
AMPS	Advanced mobile phone systems
TACS	Total access communication system
TDMA	Time division multiple access
CDMA	Code division multiple access
3GPP	3rd Generation Partnership Project
GSM	Global system for mobile communication
GPRS	General packet radio service
EDGE	Enhanced data GSM evolution
MMS	Multimedia message support
HSPA+	High speed packet access
HSDPA	High speed downlink packet access
HSUPA	High speed uplink packet access
QoS	Quality of service
HDTV	High definition television
WiMAX	Worldwide interoperability for microwave access
QAM	Quadrature amplitude modulation
IMT	International mobile telecommunications
CSI	Channel state information
CS	Compressed sensing
FDD	Frequency division duplexing
TDD	Time division duplexing
LS	Lease square
MMSE	Minimum mean square error
DPP	Dirty paper precoding
TH	Tomlinson Harashima
VP	Vector perturbation
MRC	Maximal ratio combining
ZF	Zero-Forcing
R-ZF	Regularized zero-forcing
WF	Water filling
RR	Round robin
PF	Proportional fair
PLL	Phase-locked loop
PAPR	Peak to average power ratio
SD	Sphere decoder
SIC	Successive interference cancellation
NSA	Neumann series approximation
SOR	Successive over-relaxation
ADMM	Alternating direction method of multipliers
AMP	Approximate message passing
DL	Deep learning
CNN	Convolutional neural networks
RNN	Recurrent neural networks
DNN	Deep neural networks
LSTM	Long short-term memory
NARX	Nonlinear autoregressive network with exogenous inputs
ARN	Autoregressive network
SSL	Semi-supervised learning
SL	Supervised learning
MICED	MIMO iterative channel estimation and decoding
VAMP	Variational approximate message passing
APRGS	Accelerated and Preconditioned Refinement of Gauss-Seidel
UM-MIMO	Ultra massive MIMO
SU-MIMO	Single user MIMO
MU-MIMO	Multi user MIMO
VLC	Visible light communication

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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Publications, 5g advanced, publication date, december 2022, manuscript submission deadline, 25 june 2022, call for papers.

3GPP provided the specifications of 5G in Release 15. Releases 16 and 17 provided improvements to the system performance and supported the integration of new scenarios for verticals. The upcoming Release 18 will define “5G Advanced,” a milestone towards 6G. Artificial Intelligence (AI) and Machine Learning (ML) will be new key components of 5G Advanced. They got already introduced in Release 17 for network automation, but are expected to play a more prominent role for network and service management as well as orchestration in real- and non-real-time. It is expected that AI/ML will boost the performance in all layers of the network, particularly in the orchestration of the emerging technologies of the mobile core and Radio Access Network (RAN). New services, such as Extended Reality (XR), will be considered together with the improvements of the 5G system. Efforts will be also spent on improving further other existing services and features, such as network slicing, Uncrewed Aerial Vehicles (UAV), Multi-Access Edge Computing (MEC), Non-Public Networks (NPN), Multicast and Broadcast Service (MBS), enhanced Mobile Broadband (eMBB); and Non-Terrestrial Network (NTN) integration.

Scope of Contributions

This Special Issue (SI) is intended to provide tutorial information and original research articles to the IEEE Communications Standards Magazine readers on 5G Advanced. Topics of interest include (but are not limited to):

Enhancements to UAV with system and service aspects
5G System Support for AI/ML based services
Architecture enhancements for XR and media services
MEC Enhancements
Enhanced support for non-public networks
Technological enablers for network automation
Enhancements for non-terrestrial networks and satellite access
Privacy and security enhancements for 5G Advanced
Network slicing enhancements
NR coverage and mobility enhancements
AI/ML for new generation RAN and O-RAN
AI/ML and closed loop automation in 5G advanced core
Life cycle management of AI/ML
Network exposure capabilities towards verticals
Distribute ledge technology for the 5G Advanced
Enhanced security towards zero trust
Power saving enhancements
Enhancements for enhanced Mobile Broadband
Enhanced support for Industrial IoT and Digital Twin

Submission Guidelines

Manuscripts should conform to the standard format as indicated in the Information for Authors section of the Paper Submission Guidelines. All manuscripts to be considered for publication must be submitted by the deadline through Manuscript Central. Select “December 2022/5G Advanced” from the drop-down menu of Topic/Series titles.

Important Dates

Manuscript Submission Deadline: 31 May 2022 25 June 2022 (Extended Deadline) Decision Notification: 31 October 2022 Final Manuscript Due: 15 November 2022 Publication Date: December 2022

Guest Editors

Andreas Kunz Lenovo/Motorola Mobility

Tarik Taleb University of Oulu

George Alexandropoulos National and Kapodistrian University of Athens

Konstantinos Samdanis Nokia

JaeSeung Song Sejong University

Support the future of cybersecurity

Berkeley School of Information

CLTC Report: “Security Implications of 5G Networks”

A new report published by the Center for Long-Term Cybersecurity explores how the widespread adoption of fifth-generation (5G) cellular service will lead to improvements in security — and also expose new threats and attack vectors. The report, “Security Implications of 5G Networks,” is authored by Jon Metzler, a lecturer at the Haas School of Business at UC Berkeley and founder of Blue Field Strategies, a consulting firm helping infrastructure clients such as network operators. The report is based upon research and interviews that Metzler conducted with support from a CLTC grant.

Cover image - Security Implications of 5G Networks

A new report published by the Center for Long-Term Cybersecurity, “ Security Implications of 5G Networks ,” explores how the widespread adoption of fifth-generation (5G) cellular service will both bring potential improvements in security — and also expose new risks.

Authored by Jon Metzler , a lecturer at the Haas School of Business at UC Berkeley and founder of consulting firm Blue Field Strategies, the paper draws upon research and interviews conducted over a two-year period with support from a CLTC grant. The paper aims to help network operators — and their customers and partners — prepare for new risk vectors opened by 5G service, in terms of service models or network deployment models, at a critical moment in the development of 5G. “The long-lasting nature of network investments means that supplier selection decisions will have implications for decades,” Metzler writes.

Network operators around the world are rapidly expanding 5G service, and this new technology is expected to have significant advantages over prior generations, including increased speed, reduced latency (the time lag experienced by the user between a query and response), and the ability to “slice” wireless spectrum to support different applications. Yet 5G also has potential to introduce new security concerns by introducing greater diversity in suppliers, increased densification in network devices, and other factors.

The paper aims to help policymakers understand the economic and operational implications of 5G network deployment, including the switching costs of replacing suppliers and the site access needed to deploy robust, pervasive 5G networks; and to highlight security benefits of deploying both 5G RAN (which provides the wireless interface with customer devices and manages related radio resources) and core (which handles authentication, switching, interface with other networks, etc.)

As detailed in the executive summary, the paper highlights the following key points:

Networks persist. Network technologies, and suppliers, are used for decades once deployed. The switching costs that result from changing suppliers extend beyond capital investment. They also include re-training and changing operational practices. “Rip and replace” costs include these training and migration costs, and the transition itself may open security risks.
5G service will support a more diverse set of applications than traditional mobile service offered to consumers. This will add new value to 5G service as compared to prior generations. It will also raise the consequences of service outages. In this paper, this is referred to as “value at stake.”
More diverse applications may mean more heterogenous suppliers, including device and service partners outside of the traditional set of operator suppliers. While mitigating supplier dependencies (single points of failure), working with unfamiliar suppliers may open new risk vectors. This will require operators or their partners to be able to test and verify new device partners quickly to validate their security practices.
The three types of spectrum band (high-band or mm-wave; mid-band; and low-band) allocated to 5G have different implications for network topology. Mid-band and high-band service will necessitate significant densification of operator networks. This densification may open greater operational and physical access risks than do traditional cellular networks. Further, the increase of cell sites required with network densification will require robust network monitoring capability, and the ability to update and patch software on small cells and customer premise equipment.
5G networks have at least three security benefits relative to prior generations: improved authentication; distributed core; and network slicing, dividing a single network into different “slices” while using the same wireless spectrum and physical network infrastructure. Realizing these benefits requires deploying both 5G RAN and 5G core. These benefits are compelling reasons for customers to investigate 5G-only service.

Based on the above, Metzler’s paper recommends that:

Operators, their partners, and their customers investigate the viability of 5G-only service;
Operators and their partners develop the ability to rapidly deploy software updates, including security patches, to small cells, customer premise equipment, and other connected devices;
Operators and their partners develop the ability to rapidly test and verify devices from new partners from outside of the traditional telecom ecosystem;
Policymakers act to facilitate rapid deployment of 5G networks, including implementing policies to facilitate cell site acquisition;
Policymakers recognize the role of global standards bodies; of rapid standards development; and the economic value of globally harmonized standards.

“For a variety of factors, such as spectrum holdings, there has been variance in how operators have gone to market with 5G service, especially when compared with prior generations,” Metzler explains. “While this variance is potentially frustrating for consumers and device makers, it is perhaps fortunate from a security perspective. Each new market allows operators to hone their craft and become more efficient with the next rollout. The market is still early in its development. It is the author’s hope that the recommendations in this paper can be of value to operators as they build out their 5G services.”

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5G in the U.S. – Additional Mid-band Spectrum Driving Performance Gains

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5G performance in the United States continues to improve as more mid-band spectrum becomes available. In March, T-Mobile gained access to additional 2.5 GHz spectrum it won at auction 108 in 2022, and we’re already beginning to see the impact of this, adding extra capacity to its 5G network and boosting performance in rural U.S. locations in particular. In just one month, T-Mobile’s median download performance across the U.S. increased by 29.64 Mbps. Its recent agreement to acquire the bulk of US Cellular’s wireless operations and a portion of its spectrum holdings will help it further reinforce its competitive lead. Verizon and AT&T have both benefited from the early vacation of C-band spectrum by satellite providers, the licenses for which were acquired through Auction 107 in February 2021. AT&T acquired additional 3.45 GHz licenses, former U.S. Department of Defense spectrum, made available through Auction 110 which concluded in January 2022. All three major carriers have since been upgrading their sites to support their new spectrum frequencies. This update reviews the latest Speedtest Intelligence® data to highlight the impact of deployments in new spectrum bands for U.S. 5G users.

Key takeaways

Recent trends highlight the importance of additional mid-band spectrum for 5G. Speedtest Intelligence data shows a clear correlation between the release of additional mid-band spectrum, 5G performance, and consumer sentiment for 5G networks, with all three national wireless providers benefitting over the past 6 months. This sends a clear message to the FCC and other regulators, of the benefits of allocating additional spectrum for cellular use, as advocated for by industry bodies such as the CTIA, CCA and GSMA.
T-Mobile intent on holding its lead. While C-band spectrum allowed Verizon in particular to play catch-up during Q4 2023, T-Mobile has continued to build on its performance advantage and innovate, moving to a 5G Standalone (SA) architecture, testing six carrier aggregation, while also benefiting from deploying in additional mid-band spectrum starting in March. T-Mobile recorded a median 5G download speed of 287.14 Mbps as of March 2024, an increase of 29.64 Mbps in a single month, which helped it extend its lead over Verizon, which recorded 224.67 Mbps, and AT&T with 145.36 Mbps. Additional spectral capacity will also help fuel further growth of 5G Fixed-Wireless Access (FWA) services, as wireless operators have had to be selective in signing up new fixed customers in order to manage capacity.
Additional mid-band spectrum helping close the gap on regional disparities within the U.S. While the U.S. ranks highly on 5G performance, 5G Service, and 5G Availability metrics versus other leading 5G markets globally, there have remained wide disparities in 5G performance between U.S. states, and between urban and rural locations. Recent mid-band spectrum deployments are starting to shift the needle for a number of states and rural communities.
5G upload and latency performance need more attention. To date, capacity gains from additional spectrum are being directed almost universally to boost 5G download performance, in part because 5G-NR TDD radios are being used in both 2.5 GHz and 3.5 GHz bands. While latency remains relatively static, we do see a consistent improvement from T-Mobile, a trend which will be important if the carrier is to differentiate itself on latency-critical applications in the future.

T-Mobile continues to maintain its national lead on 5G performance

Speedtest Intelligence data for the U.S., covering the last three years, clearly shows how instrumental additional mid-band spectrum has been for all major US carriers. Four points in time stand out very clearly when we look at median download speeds across the market:

T-Mobile’s deployment of 5G in both 600 MHz and 2.5 GHz spectrum during 2021 (acquired through the merger with Sprint), giving it a significant early advantage, as AT&T and Verizon focussed more heavily on mmWave spectrum.
Verizon performance picked up in January 2022, after it began C-band deployments , which had been delayed due to concerns of interference at airports from the FAA.
The early vacation of the remaining C-band spectrum by incumbent satellite operators in August 2023, giving AT&T and Verizon full access to the spectrum frequencies they acquired at auction in 2021.
T-Mobile’s recent deployment following the release of additional 2.5 GHz spectrum as part of Auction 108, beginning in March 2024.

Chart fo U.S. 5G Median Download Speeds | January 2021 - May 2024

T-Mobile had capitalized on its early advantage, building out 5G in 600 MHz spectrum to cover 200 million Points of Presence (PoPs) as of 2020, following that up with wide deployment in its mid-band 2.5 GHz spectrum holdings. Despite performance boosts for AT&T and Verizon from additional C-band spectrum in Q4 2023, T-Mobile still led the pack with a median 5G download speed of 275.50 Mbps as of May 2024, 23% faster than next placed Verizon. Its lead had narrowed since August, with Verizon’s C-band spectrum helping it increase median 5G performance from 133.56 Mbps in June to 215.57 Mbps in December. AT&T also saw performance pick up in the second half of 2023, and at the turn of the year, these trends pointed towards a much more competitive 5G market during 2024, while also driving increased capacity for wireless provider’s 5G FWA services.

T-Mobile has continued to innovate in order to drive performance gains across its 5G network. In addition to deploying a 5G Standalone architecture, it is pushing the envelope on carrier aggregation, most recently completing a test with Ericsson and Qualcomm of six carrier aggregation , stitching together two channels of each of its 2.5 GHz, PCS, and AWS spectrum to achieve download speeds in excess of 3.6 Gbps. Furthermore, having finally gained access to additional 2.5 GHz spectrum it won during auction 108 in 2022, but had not been cleared to use, T-Mobile has rapidly been enabling the new spectrum across its footprint. This has allowed it to extend its lead in the market, recording a median 5G download speed of 287.14 Mbps in March 2024. As cellular providers ramp up their home broadband offerings via 5G fixed wireless access (FWA), as we recently highlighted , they will need to balance fixed net additions carefully in order to ensure cellular performance does not suffer, and will require additional high capacity spectrum over time to meet demand.

Driving improved quality of experience and consumer sentiment

The uplift in 5G performance is driving improved consumer sentiment, as measured by net promoter score (NPS). NPS is a key performance indicator of customer experience, categorizing users into Detractors (score 0-6), Passives (score 7-8), and Promoters (score 9-10), with the NPS representing the percentage of Promoters minus the percent of Detractors, displayed in the range from -100 to 100. Reviewing Speedtest Intelligence data shows that U.S. cellular providers returned either flat or declines in 5G NPS over the period March to August 2023. From September onwards, we see a strong uplift in 5G NPS in particular for Verizon and AT&T following their C-band deployments. T-Mobile on the other hand, has seen a sizable increase in 5G NPS in March, corresponding to its deployment in additional mid-band spectrum.

Chart of 5G Net Promoter Scores, U.S. Wireless Providers

Key to this growth in 5G NPS for all three cellular providers, is the impact that increases in 5G performance are impacting the quality of experience for end users for key use cases such as video streaming and mobile gaming. Both measures, as highlighted by Ookla’s 5G Game Score™ and 5G Video Score™ metrics have seen strong increases over the course of the past year.

5G Video & Gaming Quality of Experience Speedtest Intelligence® | Q1 2023 – Q1 2024

Positioning the U.S. strongly internationally

Performance gains from all national cellular providers have enabled the U.S. to climb the ranks when compared to its peers internationally. Over the course of just one year, it has moved from 20th place on Ookla’s Speedtest Global Index , to reach 11th as of February 2024. This has been driven by increased availability of mid-band spectrum for 5G use, as advocated for by the CTIA, which recently released a report claiming that the U.S. could benefit from an additional $200 billion in economic growth over the next 10 years through allocating additional mid-band spectrum for 5G.

U.S. providers are also continuing to expand the reach of 5G networks across the market. 5G Service, the share of known operator locations where 5G was present (of total locations with cellular service) climbed from 68.4% in Q3 2023 to 76.7% in Q1 2024. Deployment of 5G in low band spectrum is also critical to ensuring high 5G Availability – the share of 5G users that spend a majority of their time connected to 5G networks. The U.S. still tracks as one of the leading markets globally for 5G Availability, despite its comparatively large landmass, although that metric remained level quarter-on-quarter.

5G Service and 5G Availability – U.S. vs Other Leading 5G Markets Speedtest Intelligence® | Q1 2024

Closing disparities in 5G performance between U.S. states & rural locations

While national median speeds continue to advance, there remain some significant disparities in 5G performance at an individual state level. The Midwestern States fare best, with Illinois, Kansas, North Dakota, and Minnesota all within the top-5 performing states nationally, with median 5G download speeds above 225 Mbps during Q4 2023. At the other end of the scale are U.S. states with the highest shares of rural populations, including Vermont, Maine, Mississippi, and West Virginia, which had median download speeds below 100 Mbps.

5G Median Download Speed by U.S. State (Mbps) Speedtest Intelligence® | Q4 2023

Differing allocations of spectrum, channel bandwidths, device capabilities, and carrier aggregation options all impact the observed performance of each service provider across the locations they serve. While each network operator has its own 5G deployment strategy, the deployment of mid-band spectrum for capacity in urban locations, complemented with sub-1 GHz spectrum to enable wider and better 5G coverage, is the common approach. While performance gaps will remain as a result of these deployment strategies, recent mid-band spectrum deployments, including in C-band and 2.5 GHz, are beginning to help close the performance gap for some states.

We examined T-Mobile’s recent performance, comparing data between February and March, as it deploys 5G in its additional 2.5 GHz spectrum. The results show performance has increased across a wide range of U.S. states, with its median 5G performance increasing by more than 10 Mbps within 35 States and the District of Columbia. Among the ten states with the lowest median 5G download speed (based on data for all providers), T-Mobile showed the most significant performance uplifts in West Virginia (+79.73 Mbps), Wyoming (+66.61 Mbps), and New Hampshire (+48.50 Mbps).

T-Mobile’s 2.5 GHz Dividend – Uplift in 5G Median Download Speeds (Top 15 Improving States) Speedtest Intelligence® | March vs February 2024

Speedtest Intelligence data also illustrates the gap between rural and urban U.S. locations , which has widened over the last three years as mid-band deployments have tended to focus on more urban locations. That is beginning to change, with both T-Mobile and Verizon keen to highlight the impact of their recent spectrum deployments on rural 5G performance. While AT&T only saw a minor increase in median 5G download speeds in rural locations, both T-Mobile and Verizon have targeted significant increases in performance through mid-band spectrum deployments.

Mid-band spectrum driving improvements in urban & rural 5G performance Speedtest Intelligence® | Q1 2021 – Q1 2024

All eyes on download throughput – upload & latency require attention

Additional spectrum has fueled surges in download performance thanks to the deployment of 5G in mid-band spectrum, but upload and latency metrics have not improved to the same degree. All three cellular providers maintained relatively static median upload speeds across the two year period we examined (Q1 2022 to Q1 2024). 5G latency performance was a mixed picture, with T-Mobile the only provider to consistently improve, reducing its latency from 55 ms in Q1 2022 to 46 ms in Q1 2024. Both Verizon and AT&T saw latency grow over the same period.

5G Median Upload and Latency Performance, (by provider, U.S.) Speedtest Intelligence® | Q1 2022 – Q1 2024

It’s very clear that U.S. cellular providers are prioritizing improvements in download performance. This will likely continue in 2024, as T-Mobile, AT&T, and Verizon each seek to gain the upper hand, using any 5G network advantages to capture a larger share of competitive churn. Over time however, we expect the relative importance of upload and latency performance to grow, as 5G download performance begins to exhibit diminishing marginal returns, and increasing importance is given to improving the experience of latency-sensitive use cases such as video calling, mobile gaming, and augmented reality.

2024 is set to drive renewed competitive pressure between all of the service providers in the U.S., with the continuing deployment of 5G in mid-band spectrum, T-Mobile’s acquisition of US Cellular’s assets, and made all the more interesting given the DISH wildcard. We’ll continue to monitor and report on 5G performance trends in the U.S., and their impact on Speedtest users. To learn more about Ookla Speedtest Intelligence, please get in touch .

Ookla retains ownership of this article including all of the intellectual property rights, data, content graphs and analysis. This article may not be quoted, reproduced, distributed or published for any commercial purpose without prior consent. Members of the press and others using the findings in this article for non-commercial purposes are welcome to publicly share and link to report information with attribution to Ookla.

About the Author

Mark Giles is the Lead Industry Analyst at Ookla

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Research areas in 5G technology
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Study and Investigation on 5G Technology: A Systematic Review
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The fifth generation (5G) organize is required to help essentially enormous measure of versatile information traffic and immense number of remote associations. To accomplish better spectrum, energy-efficiency, as a nature of quality of service (QoS) in terms of delay, security and reliability is a requirement for several wireless connectivity. Massive Multiple-input Multiple-output (mMIMO) is ...
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5G Hardware Components: Advancements and Future Trends
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Using 5G in smart cities: A systematic mapping study
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Editorial: 5G-and-Beyond Communications for Smart Cities: Networks
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A Study on 5G Technology and Its Applications in Telecommunications
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CLTC Report: "Security Implications of 5G Networks"
Download the Report. A new report published by the Center for Long-Term Cybersecurity, "Security Implications of 5G Networks," explores how the widespread adoption of fifth-generation (5G) cellular service will both bring potential improvements in security — and also expose new risks. Authored by Jon Metzler, a lecturer at the Haas School of Business at UC Berkeley and founder of ...
5G in the U.S.
5G performance in the United States continues to improve as more mid-band spectrum becomes available. In March, T-Mobile gained access to additional 2.5 GHz spectrum it won at auction 108 in 2022, and we're already beginning to see the impact of this, adding extra capacity to its 5G network and boosting performance in rural U.S. locations in particular.

5G Summit

	19 January 2017
	17 March 2017
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	11 May 2017
	May 2017
	5-6 June 2017
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	27-28 July 2017
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	14 October 2017
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	29 March 2016
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	26 May 2015
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