5g technology thesis topics

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Study and Investigation on 5G Technology: A Systematic Review

Ramraj dangi.

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

Praveen Lalwani

Gaurav choudhary.

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

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

Giovanni Pau

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

Associated Data

Not applicable.

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

1. Introduction

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

1.1. Evolution from 1G to 5G

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

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

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

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

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

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

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

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

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

Summary of Mobile Technology.

Table of Notations and Abbreviations.

1.2. Key Contributions

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

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

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

2. Existing Surveys and Their Applicability

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

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

2.1. Limitations of Existing Surveys

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

Systematic layout representation of survey.

2.2. Article Organization

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

3. Preliminary Section

3.1. emerging 5g paradigms and its features.

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

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

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

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

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

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

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

3.2. Commercial Service Providers of 5G

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

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

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

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

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

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

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

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

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

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

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

3.3. 5G Research Groups

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

Research groups working on 5G mobile networks.

3.4. 5G Applications

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

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

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

4. 5G Technologies

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

4.1. 5G Massive MIMO

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

Highlights of 5G Massive MIMO technology are as follows:

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

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

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

State-of-the-Art Approaches

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Highlights of 5G NOMA technology as follows:

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

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

State-of-the-Art of Approaches

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

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

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

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

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

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

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

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

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

Summary of NOMA-based approaches in 5G technology.

4.3. 5G Millimeter Wave (mmWave)

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

Highlights of 5G mmWave are as follows:

Pictorial representation of millimeter wave.

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

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

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

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

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

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

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

4.4. 5G IoT Based Approaches

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

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

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

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

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

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

Highlights of 5G IoT are as follows:

Pictorial representation of IoT with 5G.

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

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

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

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

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

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

Summary of IoT-based approaches in 5G technology.

4.5. Machine Learning Techniques for 5G

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

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

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

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

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

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

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

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

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

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

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

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

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

4.6. Optimization Techniques for 5G

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

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

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

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

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

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

Summary of Optimization Based Approaches in 5G Technology.

5. Description of Novel 5G Features over 4G

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

5.1. Small Cell

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

Types of Small cells.

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

Pictorial representation of communication with and without small cells.

5.2. Beamforming

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

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

Pictorial Representation of communication with and without using beamforming.

5.3. Mobile Edge Computing

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

Pictorial representation of cloud computing vs. mobile edge computing.

6. 5G Security

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

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

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

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

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

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

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

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

8. Conclusions

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

9. Future Findings

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

Acknowledgments

Author contributions.

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

This paper was supported by Soonchunhyang University.

Institutional Review Board Statement

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

The authors declare no conflict of interest.

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

e-mail address: [email protected]

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5G Research topics offer scholars an opportunity to innovate and explore the field of 5G Wireless systems using enhanced algorithms and protocols to increase the data rates and overcome the existing challenges. 5G networking is the advancement in the wireless domain for next-generation needs to offer enhanced data rates. Every new technology overcomes the existing challenges; similarly, 5G networks offer superior speed than the existing LTE/4G Networks to manage and connect multiple devices. Before taking up research in 5G Networks , we must have some basic theoretical and subjective knowledge. So, let’s get some technical insights into the 5G network, 

PhD Research Areas in 5G Network

• Ubiquitous / Pervasive Communications 
• Evolution of Smart Cities & Industries  
• Implementing Integrated IoT 
• Cognitive Radio Oriented Wireless Networks
• Enhanced Integrated Network Security  
• Cloud Computing with Fog/Edge Networking

Top 7 Research Topics in 5G Technology

Below we have mentioned Interesting 5G Research Topics , reach us to know more information about latest 5G Research work.

• Implementing Security mechanism in Pervasive/Ubiquitous Computing
• Evolution of Future Wireless Networks using Cognitive Radio 
• Implementing Privacy and Security in Wireless Networks
• Elevated Performance Network Virtualization
• Experimental Results on Spectrum effectiveness in End-to-End wireless systems
• D2D in 5G Network Architecture, Supervision Techniques, and Services
• Joint Management and Orchestration of Networking with Cloud Technologies 

Benefits of 5G Network over 4G Network

• Handle multiple devices to stay connected with enhanced data speed 
• Enhanced data rate and throughput compared to the existing network.
• Encompassing advanced technological need, i.e., Communication with Cloud services like Microsoft Azure and Amazon web services
• Offers reduced latency i.e., seamless response time for each client request. 

To get some insight about 5G Research topics , we have provided few latest 5G Research Titles below. These are few topics mined with the help of top researchers and experts. Scholars can reach us directly to get more information about each topic. We also provide support to Scholars who are completely new to the field of research. Our support will start from topic selection and end till thesis completion . 

How does 5G work?

5G Networks employs  radio frequencies  that involve frequent updates of radio and other connected equipment over cell phone towers. Such design implementation can be done using the below-mentioned way-

• Low band Network [20% superior speed as compared to 4G but offers vast coverage ]
• Mid band Network [Balanced coverage and speed]
• High band Network [Offers super-fast data transfer rate but handover challenges]

A network Simulation tool has its own significance as it determines the Network performance, i.e., required topology, and used to evaluate novel algorithms . It provides a platform to evaluate the overall network prototype before its practical implementation, which curtails the hardware implementation cost. 

Different circumstances require different network simulators to evaluate the actual theory and assumption. One must understand the Simulation tools to opt for the optimum Simulator for their requirement to consider which tool will yield optimum result for 5G Research Topics chosen. Considering this fact, we have provided few major Simulators and their specifications for scholars’ reference.

What are the 5G network simulators? Top 4 Tools to implement 5G Research Work

• NS-3 [Open Source software implemented using C++ / Python and supported Linux, Windows, and MAC OS]
• RiverBed [C/C++ based Commercial software, Supports Linux and Windows]
• OMNet++ [C++ based Open source software, Supports Linux, Windows, and MAC OS]
• NetSim [Windows based Propriety Software implemented using C++ and Java]

Other Significant Network Simulators 

• NetTest 5G Network Emulators
• 5G NR Simulation Tool

To be particular, let’s understand the above mentioned Simulator – NETSIM features to get an in-depth perception to implement 5G research work .

Key Features of Network Simulator

• Inspection and management of Simulation using DES [Discrete event simulation], which offers event-level debugging
• Drag and drop feature to get enhanced GUI support
• Advanced result dashboard along with packet animator
• Offers round the trip simulation support for 5G Networks
• Detailed tracing along with NR log files using packet-level simulation 
• Standalone structural design with supported Application Models: Voice, HTTP, Custom, FTP, and Video
• C Code supported protocol along with 5G library interface [NetSim TCP/IP stack] to offer simulation competence
• Supported Devices are gNB, Router, EPC, Switch, UE, and Server

Understanding each Simulator is easy, but choosing the best network simulator among it as per the requirement would be a complex task for researchers.

5G Research [Guidance]

Based on the 5G Research Project requirement, our Specialist will guide and provide comprehensive details about the Simulation tool that would cost the scholars optimum. Our support doesn’t stop with tool selection or implementation; we offer complete End to End support until the scholars achieve their Research goal. Further, we offer PhD and MS Scholars research guidance in all domains encouraging in-depth research and investigation.

 We expect our scholars to commit to us and bring their requirements to experience our work quality and massive support system in choosing 5G Research Topics for your thesis work. We hope scholars have a wide scope in 5G domain due to its presentation of consistent technological advancement and emerging networking needs. 

WORLD GET CONNECTED THROUGH 5G technologies, WE GET CONNECTED To make our scholars hassle free,                         Contact us for your 5G network project topics!!!!!!!!         

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Charting an integrated future: IoT and 5G research papers

The fifth-generation cellular network (5G) represents a major step forward for technology. In particular, it offers benefits for the network of interrelated devices reliant on wireless technology for communication and data transfer, otherwise known as the Internet of Things (IoT). 

The 5G wireless network uses Internet Protocol (IP) for all communications, including voice and short message service (SMS) data. Compared to earlier networks, such as 3G and 4G, it will have higher response speeds (lower latency), greater bandwidth, and support for many more devices. 

Every sector is using some form of wireless-enabled technology. Low latency plays a critical role in many IoT applications where a lag in data transfer to an IoT device can mean a disruption in the manufacturing process, a crashed car, or a disrupted power grid. Increased capacity to support IoT devices means more of the world’s population will be able to access the global digital economy. 

Yet with more capability comes more complexity, and there are challenges to making 5G connection a full reality. There is global interest in realizing the potential of 5G and IoT integration. Research papers on a wide array of topics are helping to advance the field and bring the vision of 5G technology and IoT connectivity into focus. 

Realizing the potential of 5G and IoT through research

The 5G network represents the best chance for an ever-growing array of wirelessly connected devices to realize their full potential . 

Making the case for 5G technology

Using millimeter wave technology, 5G connectivity offers increased speed, bandwidth, and reliability of data transfers. These improvements mean that more computing power can be pushed to the cloud, clearing the way for smaller, cheaper, and simpler devices that can do more. Smartphones are a great example of how increased wireless network capacity has allowed devices to get smaller while increasing the range of a user’s cloud-based activities. 

The 5G mobile network also has social justice implications. As Brookings Institute senior fellow Nicol Turner Lee discusses in her research paper “ Enabling Opportunities: 5G, the Internet of Things, and Communities of Color ,” the development of wireless networks will factor heavily in whether mobile-only users can fully participate in the global digital economy. 

Universal benefits, inspired innovations

The 5G network could spur additional IoT innovations such as the following:

• Advancements in edge computing
• Creation of smart cities, smart power grids, and expanded functionality of smart homes
• Improvements in health-care monitoring and delivery of services
• Retail improvements
• Real-time remote control of robots that could improve farming efficiency
• Automated manufacturing
• Supply chain improvements
• Improved transportation and self-driving cars 
• Expanded use of artificial intelligence reliant on machine learning
• More cloud computing
• Expansion of virtual reality and augmented reality

While work to build out 5G has begun, many of the challenges and logistics of completing this vast network still need to be resolved. Some of the challenges include the following:

• Managing disruption to the radio transmission
• Network and wireless security
• Connectivity issues from the network to the internet (known as “backhaul”)
• Assuaging concerns over health impacts of increased high-speed electromagnetic energy
• Cost and logistics of building a vast network of towers across different governmental jurisdictions

Those with a stake in making 5G a reality are investing in researching solutions that explore the possibilities and challenges of 5G deployment and IoT integration. Research is also emerging on how 5G and IoT technology can be utilized to respond and fight the COVID-19 pandemic. 

Two halves of a whole—the relationship between IoT and 5G

5G is revolutionary in that it replaces hardware components of wireless networks with software components that offer increased system flexibility. In doing so, it delivers more power to wireless devices that rely upon fast, uninterrupted data transmission. 

Making IoT smarter

Artificial intelligence (AI) technology, which plays heavily in many IoT applications, relies on smooth and frequent transmission of data. Every disruption in the data transfer process interrupts the feedback loop that facilitates machine learning. 5G’s lower latency eliminates these data hiccups, which translates to better performance over time. 

The 2019 paper “ AI Management System to Prevent Accidents in Construction Zones Using 4K Cameras Based on 5G Network ,” published in the IEEE Xplore digital library, examines how workplace safety can be improved through AI technologies running on the 5G wireless platform. 

Critical and massive IoT

There are two types of IoT devices: Critical IoT devices offer low latency, high uptime benefits. They facilitate bandwidth-hungry applications that include telemedicine, first responder applications, and factory automation. Massive IoT refers to a network of lots of devices using little bandwidth or speed. These devices find use in applications such as wearables, smart agriculture, smart homes, and smart cities. 

5G technology also allows a service provider to dedicate portions of their networks for specific IoT applications. Known as network slicing, the ability to segment a set of optimized resources further improves the ability of 5G to respond to the varying data and bandwidth needs of critical and massive IoT applications. 

The recent paper “ Secure Healthcare: 5G-enabled Network Slicing for Elderly Care,” published in the IEEE Xplore digital library, provides insight into the existing limitations in elder care and discusses a solution that encompasses 5G network slicing techniques and innovations. 

Cybersecurity on the 5G

One fundamental difference between 5G and its predecessors is the shift from a hardware-based system to a software-based system. This shift presents new security challenges as software is more vulnerable to hacking—the same wireless pathways over the 5G that enable IoT can be used to breach it, whereas to hack hardware you need direct physical access. 

Technical solutions to expanding capacity while increasing IoT security, such as those that the IEEE paper “ Wideband Antennas and Phased Arrays for Enhancing Cybersecurity in 5G Mobile Wireless ” discusses, are being researched and discussed worldwide. In addition, the Brookings Institute’s 2019 research paper “ Why 5G Requires a New Approach to Cybersecurity ,” discusses why developing coordinated cybersecurity public policies is of paramount importance.

Investing in the future—top research projects on IoT and 5G integration

Governments and the private sector, including trade associations, service providers, and major tech players are funding research at academic institutions. For example, the University of Texas at Austin’s Wireless Network and Communications Group has an Industrial Affiliates Program that allows companies like Huawei to become stakeholders in the center and to participate in the growth and direction of its research on millimeter waves. Similarly, New York University’s Brooklyn engineering program partners with Nokia, Intel, and AT&T to support its research. 

In the US, the National Science Foundation is supporting advanced wireless research. Research England’s UK Research Partnership Investment Fund (UKRPIF) supports 5G research, including that being done at the University of Surrey’s 5G Innovation Centre . Nonprofit organizations, such as the Brookings Institute , are also conducting research on the logistics and impacts of 5G and IoT. 

Universities, companies, and organizations such as IEEE regularly team up to host conferences around the world that showcase all aspects of 5G. IEEE’s Future Networks is dedicated to enabling 5G and regularly calls for papers related to 5G. 

Opportunities for 5G and IoT—building a sustainable future

The ultimate goal of 5G and IoT integration is for everything to be connected more simply on smaller, less expensive devices. The 5G network has the potential to drive advancements in IoT and to fundamentally change the way humankind operates around the globe with long-term positive impacts possible with respect to sustainability. 

In practical terms, the 5G network provides better efficiency through increased control. At the local level, a smart city would be better able to monitor, through IoT applications, public safety and utilities. This would mean greater conservation and a reduction in their overall carbon impact while improving the lives of its residents. 

As Darrel M. West examines in his paper “ Achieving Sustainability in a 5G World ,” IoT innovation in the energy, manufacturing, agriculture and land use, buildings, and transportation sectors coupled with full 5G deployment could allow the global community to meet our long-term sustainability goals. 

Want to learn more about the latest IoT and 5G research? Participate in the 2020 IEEE 3rd 5G World Forum (5GWF'20). The virtual conference, which will be available from September 10–12, aims to bring together experts from industry, academia, and research to exchange their vision as well as their achieved advances towards 5G. In addition, it aims to encourage innovative cross-domain studies, research, early deployment, and large-scale pilot showcases that address the challenges of 5G.

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5G Implementation: Major Issues and Challenges

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Massive MIMO Systems for 5G Communications

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• Published: 08 May 2021
• Volume 120 , pages 2101–2115, ( 2021 )

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• Sinan A. Khwandah   ORCID: orcid.org/0000-0003-4668-6864 1 ,
• John P. Cosmas 4 ,
• Pavlos I. Lazaridis 2 ,
• Zaharias D. Zaharis 2 &
• Ioannis P. Chochliouros 3  

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Massive MIMO will improve the performance of future 5G systems in terms of data rate and spectral efficiency, while accommodating a large number of users. Furthermore, it allows for 3D beamforming in order to provide more degrees of freedom and increase the number of high-throughput users. Massive MIMO is expected to provide more advantages compared to other solutions in terms of energy and spectral efficiency. This will be achieved by focusing the radiation towards the direction of the intended users, thus implementing simultaneous transmission to many users while keeping interference low. It can boost the capacity compared to a conventional antenna solution, resulting in a spectral efficiency up to 50 times greater than that provided by actual 4G technology. However, to take full advantage of this technology and to overcome the challenges of implementation in a real environment, a complicated radio system is required. The purpose of this work is to present the MIMO technology evolution and challenges in a simple introductory way and investigate potential system enhancements.

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Avoid common mistakes on your manuscript.

1 Introduction

The cellular infrastructure evolution has started from 1G analog systems in the 1980s followed by 2G digital systems like GSM, where the baseband unit, a digital unit connected to the telecom network, was co-located with the analog radio head unit in a shelter at the base of the antenna tower. Thick low-loss coaxial cables were used to connect these units to the antennas on the top of a tall tower with amplifiers to compensate for the loss of power along the coaxial cables. In recent 3G and 4G systems, distributed networks are used instead of traditional networks in 1G and 2G. In distributed networks, the radio unit, including all transmitting and receiving components with amplifiers, is split from the baseband unit and is placed on the top of the antenna tower. Long running coaxial cables that are lossy at high frequencies are replaced by fiber fronthauls carrying digital data up to the tower. This architecture achieves an important link gain because the radio head is placed next to the antennas. In 4.5G and 5G systems, the baseband processing unit will be centralized [ 1 ]. The remote radio unit will be directly integrated with hundreds of antenna elements, thus forming the massive MIMO scheme (Fig.  1 ).

Antenna array for mm-Wave frequencies and the cellular infrastructure evolution

The dramatic increase of wireless data traffic across the world puts significant pressure on the existing wireless communication systems. The limitation of spectrum availability has directed telecom engineers towards mm-Wave frequencies, which require small radiating elements [ 2 ]. This scaling down of the antenna element size perfectly suits the requirement of massive MIMO, and makes these large-scale antenna arrays a promising technology [ 3 ]. Furthermore, an increased number of antenna elements leads to a better performance and an improved signal to interference-plus-noise ratio (SINR). In conventional MIMO systems, the maximum number of antenna elements used is 8 at the transmitter and another 8 elements at the receiver side (8 × 8 MIMO system). However, in massive MIMO and depending on the implemented prototype, for 5G new radio, the supporting BS antennas will be up to 256 and UE antennas will be up to 32 [ 4 ]. By increasing the number of elements of the antenna array, significant throughput and coverage improvements in cellular networks can be achieved. Moreover, the higher path loss due to high frequencies can be overcome by using multiple antenna elements to combine energy in required directions. This introduces beamforming techniques into MIMO and thus radio energy is concentrated in smaller angular sectors resulting in significant spectral efficiency improvement. With the introduction of massive MIMO, a new promising technique has emerged by additionally exploiting the elevation angle. This is called 3D MIMO and it is implemented by deploying antenna elements in both horizontal and vertical dimensions. To achieve 3D MIMO, 3D beamforming must be implemented, allowing in this way the base station to adapt and dynamically control the transmission directions in both azimuth and elevation. This helps improve the system performance and accommodates the increasing capacity demand [ 5 ].

In order to identify the requirements and motivations of this advanced MIMO technology, this tutorial briefly addresses how massive MIMO evolved and why it is necessary for future 5G networks. This will be presented together with the critical technology of massive MIMO antenna arrays for evolving 4G into 5G and the challenges facing its deployment and measurement. Furthermore, practical issues and implementation based on real devices and in different environmental conditions are discussed.

In the following sections, first MIMO basics are introduced and then the advanced aspects are presented and analyzed. These aspects include the importance of beamforming, the importance of channel state information feedback, the pattern/polarization antenna array model for massive MIMO systems and finally 3D MIMO.

2 Single User-MIMO & Multiple User-MIMO

The simplest implementation of MIMO is the single-user MIMO (SU-MIMO) (release 8), where both the user equipment (UE) and the base station (BS) use multiple antennas. This enables them to support different types of transmission modes depending on channel conditions (Fig.  2 ). There are two distinct cases: transmit diversity where the same data is transmitted by multiple antennas simultaneously to boost SNR, and spatial multiplexing where independent data streams are sent on each antenna to increase capacity [ 6 ].

SU-MIMO vs. MU-MIMO

SU-MIMO is used today in LTE networks. The complexity of this scheme is based on the UE receiver because it needs to separate the different received data streams (interference due to multi stream transmission). For example, knowledge of the channel matrix H (that is a matrix which is made up of channel path coefficients) is not required at the transmitter side but it is required at the receiver side to decode the received signals. The receiver develops the channel knowledge by decoding the preamble or pilot data. This requires a lot of computational power to calculate the inverse channel matrix H −1 at the receiver side. However, performing all these calculations is not efficient when the receiver is a mobile device due to battery constrains. In MU-MIMO part of the complexity is moved to the base station, which has more computational power. In this case, a matrix W is applied at the transmitter side and performs a pre-weighting of the data [ 7 ]. To compute matrix W , during the channel coherence time, all the UEs transmit a number of uplink pilots at the same time. All the antennas of the base station receive the transmitted uplink pilots and compute the pre-weighting matrix W . The measured magnitude and phase due to the distance from each user to each element of the antenna array are used to precode the data streams and to distribute data streams to each antenna port. As a result, the receiver does not need to handle the multiuser spatial layer separation and each UE receives the data independently from the other UEs with an improved SINR.

3 From MU-MIMO to Massive MIMO

Given that different SINR requirements are set for different users depending on the application (e.g., video downloading, internet browsing, texting, etc.), beamforming is vital to the system in order to achieve different SINRs over different users inside the same cell. Utilizing beamforming techniques in mm-Wave wireless communication systems aims to find the optimal path with a minimum loss to reach users and keep interference as low as possible.

Before proceeding to beamforming, we will introduce beamsteering (Fig.  3 a), which is performed by steering the antenna main lobe towards one of a number of predefined directions [ 8 ]. To decide which direction has to be used at the transmitter, several methods are applied such as transmitting orthogonal pilot signals on each of the predefined directions. Then, the UE reports back to its serving cell BS on which signal is received with the highest power. Using beamsteering helps to improve signal strength levels in omnidirectional antenna cells, because signal strength decreases from the cell center towards the cell edge. By directing all the energy in a particular direction, SINR is significantly improved at the cell edge, moreover, several independent cells operating in the same region can be co-located without any form of co-ordination or cooperation and placed closely together (Fig.  3 b). In this case, all the transmit antennas are co-located at the same place using beamsteering to direct the energy in a particular direction. Although co-locating improves the SINR at the cell edge, however, the side lobes are a matter of concern due to potential interference. This requires antennas with significantly reduced side lobes [ 9 ].

Massive MIMO scheme

On the other hand, in beamforming, channel estimation is used. Antenna weighting compensation is applied in real time to form a beam directed towards the user of concern, instead of picking up one of a pre-defined directions as is the case in beamsteering.

In order to achieve beamforming, the number of antennas available at the base station must be at least ten times greater than the number of single antenna UEs located inside the cell [ 10 ]. The new structure of this large-scale MIMO (massive MIMO) is close to the case of co-location and enables the transmitters to focus the energy over a very narrow area through beamforming. By increasing the number of antennas, the power is better focused in a narrower beam and therefore at the receiver side, for any user, there is a higher signal level for that user while the other users receive lower signals as shown in Fig.  3 c. Beamforming results in significantly less wasted power in the coverage area. This, in turn, results in less interference and increases the spectral efficiency when using spatial multiplexing. This further helps to achieve important power savings [ 11 ].

The time required to estimate the channel (also known as channel training) of the massive MIMO system depends on the number of base station antennas used. The number of pilots received by these antennas is a function of the number of UEs and the base stations are trained in parallel as they measure signals from different UEs. Also, the number of user terminals is scaled with coherence time. As coherence time increases, more users can be served in a cell.

In the case of channel training of a Time Division Duplex (TDD) system, a channel state information (CSI) parameter is used in the communication between the UE and the network in order to describe the channel quality and recommend a proper precoding matrix [ 12 ]. The CSI of the downlink channel is estimated by performing measurements on the uplink channel by using the transmitted pilots from the users [ 13 ]. Therefore, the system makes use of channel reciprocity in the case of TDD [ 14 ]. In a Frequency Division Duplex (FDD) system, the uplink and downlink channels are independently trained. As a result, the time required for training the downlink channel is proportional to the number of antennas. Therefore, the use of large antenna arrays is not likely to work well with FDD. Thus, a viable alternative approach is needed to provide CSI estimation in an FDD system [ 15 ].

4 Scheduling and Feedback for Massive MIMO

In an Orthogonal Frequency Division Multiplexing (OFDM) system, reference signals (RSs) are used to estimate the channel [ 16 ]. In the feedback loop shown in Fig.  4 , the UE computes a Rank Indication (RI) which expresses the number of streams of information that the mobile can handle at the same time and over the same frequency resources [ 17 ]. If the SNR is low, the mobile will return a low RI parameter value. In this case, the same stream will be sent by all the antennas of the base station and this can enhance coverage.

UE feedback scheme

The channel quality indicator (CQI) is a parameter used to describe SNR and it is reported by the UE to the base station. Then, CQI is translated to an appropriate Modulation and Coding Scheme (MCS) index at the base station. The MCS index indicates the modulation order and code rate that should be used to maintain the link quality.

Furthermore a precoding matrix indicator (PMI) is used to report the best precoding matrix to the base station [ 18 ]. It tells the base station how to precode the information prior to transmission to each of the antennas so that the UE can best recover this information. Therefore, PMI is an index to a codebook. There are entries in that codebook, which describe the process of distributing or interleaving the information between the antennas. In this way, the UE is facilitated by avoiding complex channel calculations and thus MIMO is implemented in a more reliable way [ 19 ]. The UE knows about the channel conditions due to transmitted reference signals from the BS antennas. The PMI parameter defines the precoding matrix used by the base station for the current transmission for both SU-MIMO and MU-MIMO.

For a more effective estimation of the channel state information, two new reference signals have been introduced in LTE release 10 TM9 (eight layer spatial multiplexing) [ 20 ]. These are the Channel State Information Reference Signal (CSI-RS), which is common to all users in the cell, and the Demodulation Reference Signal (DMRS), which is specifically dedicated to each UE. CSI-RSs are used by the UE to calculate and report the CSI feedback (CQI/PMI/RI), while the DMRSs are used for signal demodulation and are already used for beamforming in Release 9 (Dual layer beamforming).

The performance of MIMO systems is limited by accurate CSI measurements and feedback. Therefore, the key technology enhancements include improving the precoding matrix feedback accuracy by employing analog CSI feedback and faster CQI feedback [ 21 ]. This will reflect the real channel quality, when CQI is measured on pre-scheduled CSI-RSs.

While scheduling in MU-MIMO is based on assigning a set of channel vectors to the UEs and depends on the accuracy of CSI, it is recommended for massive MIMO to consider the two-stage UE scheduling and feedback method (Fig.  5 a), where the UEs are grouped into a number of groups and the channel feedback is reported per group. Figure  5 b and c show a comparison between the two-stage scheme and other conventional methods, such as full CSI (where the base station collects CSIs of all UEs) and partial CSI feedback approaches [ 22 ]. As shown in these figures, in the partial CSI feedback approach, the amount of feedback is relatively small but the downlink transmission performance is generally lower than that of the full CSI approach. On the other hand, the two-stage scheme results in a lower amount of feedback compared to partial CSI method but achieves a spectral efficiency very close to that of the full CSI feedback approach.

a The two-stage UE scheduling and feedback b Spectral efficiency comparison c Amount of feedback comparison

5 Massive MIMO Channel Capacity Improvement

The increase in the degrees of freedom, i.e., the flexibility of the base station antenna array to direct the beams when the number of antennas at the base station is much larger than the precoding vectors, is important and it will be considered for the antenna array model for massive MIMO [ 23 ]. The use of pattern/polarization antenna array in future massive MIMO system will help improve the spectral efficiency of the system. The idea is to benefit from two schemes. The first one is Beam Division Multiple Access (BDMA), which creates a number B of sectors through 3D beamforming instead of a single sector [ 24 ], while the second one is the pattern/polarization scheme. A combined scheme is called Pattern/Polarization Beam Division Multiple Access (P 2 BDMA). Figure  6 a shows four different types (K = 4) of pattern/polarization antenna elements denoted by the O, △, □, ◇ symbols. The pattern/polarization antenna array is decomposed into K virtual antenna arrays and each one of them produces a beam distributed in B sectors. If N is the number of UE antennas, then N by K channels can be realized per sector, and (N, K) symbols can be transmitted at least in the same time/frequency resource.

a Pattern/polarization antenna elements for massive MIMO b Downlink spectral efficiency vs. SNR

An increase in the possible polarization patterns in future massive MIMO will result in even more degrees of freedom [ 23 ]. This leads to a higher spectral efficiency which in turn improves the channel capacity by exploiting the low correlation characteristics of different pattern/polarization antenna channels. Figure  6 b presents four different systems: the first one is a single 120° sector of the conventional three sector system (N = 4). The second is an 8 sector system composed by a single antenna array with vertical polarization only (BDMA: K = 1, N = 1). The third one is an 8 sector system composed by two virtual antenna arrays, one with vertical polarization, and the other with horizontal polarization (BDMA: K = 2, N = 2). Finally, the fourth one is an 8 sector system composed by four virtual antenna arrays with different pattern/polarizations from each other (P 2 BDMA: K = 4, N = 4).

As mentioned earlier, a large number of antenna elements are used in massive MIMO to generate the beam pattern. Due to the increase in the number of antenna elements, there is a concern on the amount of feedback that can cause serious overhead to the system. A proposed solution to this problem is the antenna grouping scheme (AGS). According to this scheme, the CSI is aggregated from groups of antenna elements and it is fed back by the UE.

The AGS is shown in Fig.  7 , where the UE reports only 4 values by aggregating the CSI from four groups containing 16 antenna elements in total. To increase the functionality of this scheme, it is important to determine the assigned groups. For example, antennas that have the same radiation/polarization pattern are grouped and the appropriate pattern/polarization is selected based on the channel information. In this way, different pattern/polarization schemes can be exploited over the same time/frequency resource [ 25 ].

Antenna grouping (left) and pattern/polarization grouping (right)

In order to meet the future demands of massive MIMO, a promising technology is 3D MIMO and it aims to solve the fitting problem of a large number of antenna elements (32 for example with a spacing of 0.5 λ) into the limited space available. In a 3D MIMO system (Fig.  8 a), the radiation pattern can be controlled with respect to azimuth and elevation angles by properly modifying the excitation weights of the antenna array elements.

a . Azimuth and elevation beamforming b Non-precoded CSI-RS (top) vs. beamformed CSI-RS (bottom)

When the elevation is exploited together with the azimuth, a better average-cell and cell-edge performance is obtained compared to the conventional MIMO systems used in 4G-LTE networks [ 26 ]. TDD 3D-MIMO uses channel reciprocity and the gain is higher in field trials compared to the traditional 2D-MIMO. However, the above advantages require accurate operation in three dimensions and cause new challenges [ 27 ].

In order to support elevation beamforming with FD-MIMO and have a more precise control of the beamforming direction, transceiver units (TXRUs) are introduced to the transmitter to control the amplitude and phase of the excitation weights. However, a large number of TXRUs induces a great overhead of CSI-RS and an excessive CSI-RS resource consumption [ 28 ]. This requires high resolution CSI feedback and CSI reporting mechanism with finer granularity of amplitude and phase to further improve the performance [ 29 , 30 ]. The performance and feasibility of elevation beamforming for 3D MIMO has been investigated and it has been reported that different schemes (Fig.  8 b) are beneficial for 3D MIMO and exhibit different throughput gains, while the best choice depends on the number of TXRUs [ 31 ]. Such schemes are the non-precoded scheme, where different CSI-RS ports use the same wide beam, the beamformed scheme, where CSI-RS ports use narrow beams that do not cover the entire cell, and the hybrid scheme, i.e., non-precoded & beamformed CSI.

In order to achieve the expected gain for 3D-MIMO, future systems must avoid mismatches and comply with legacy systems [ 32 ]. Therefore, as far as 3D-MIMO is concerned, the specification of the system is still under enhancement and there still exists a large margin for improvement [ 33 , 34 ].

3D MIMO improves the cell spectrum efficiency so that more users can be scheduled by eNB. As the number of connected users increases, there is a higher demand for reference signals by the users and eNBs to estimate the channel. Therefore, the requirement for reference signal capacity would become higher, or else the actual supportable users would be limited in number [ 35 , 36 ].

Mobility scenarios were addressed for future MIMO in order to achieve reliable transmission schemes. Moreover, issues concerning beam management enhancement, beam failure recovery and low-latency as well as highly reliable mm-Wave transmission are future research topics for LTE Rel-16 [ 37 , 38 ].

7 Conclusions

In this paper, we have presented the background and advantages of future massive MIMO systems. The investigation indicates good performance due to the employed methods for the downlink channel such as user grouping and group-based feedback schemes. Furthermore, the adoption of the pattern/polarization antenna array model for massive MIMO shows an increase in the degrees of freedom of MIMO channels and thus an improved channel capacity.

The extension of the current technology into 3D MIMO would upgrade the existing implementation and allow flexible azimuth and elevation radiation patterns. 3GPP has developed specification support for FD-MIMO by enhancing the MIMO relevant reference signal and CSI reporting mechanism. Finally, the uplink beamforming and channel reciprocity utilization at the UE side may be implemented as the number of antennas on UE is growing.

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Khwandah, S.A., Cosmas, J.P., Lazaridis, P.I. et al. Massive MIMO Systems for 5G Communications. Wireless Pers Commun 120 , 2101–2115 (2021). https://doi.org/10.1007/s11277-021-08550-9

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Suárez, Trujillo Luis Carlos. "Securing network slices in 5th generation mobile networks." Thesis, Brest, 2020. http://www.theses.fr/2020BRES0050.

Chang, Chia-Yu. "Cloudification and Slicing in 5G Radio Access Network." Thesis, Sorbonne université, 2018. http://www.theses.fr/2018SORUS293.

Biallach, Hanane. "Optimization of VNF reconfiguration problem for 5G network slicing." Electronic Thesis or Diss., Compiègne, 2022. http://www.theses.fr/2022COMP2707.

Motyčka, Jan. "Implementace mechanismů zajišťujících “RAN Slicing” v simulačním nástroji Network Simulator 3." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442360.

Matoussi, Salma. "User-Centric Slicing with Functional Splits in 5G Cloud-RAN." Thesis, Sorbonne université, 2021. https://accesdistant.sorbonne-universite.fr/login?url=http://theses-intra.upmc.fr/modules/resources/download/theses/2021SORUS004.pdf.

Maiorano, Picone Pasquale Carlo. "A QoS Controller Framework Compliant with the ETSI Network Function Virtualization Specification." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10406/.

Oussakel, Imane. "4G/5G cellular networks metrology and management." Thesis, Toulouse 3, 2020. http://www.theses.fr/2020TOU30261.

Tontini, Giacomo. "Orchestrazione dei servizi di rete 5G su container." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23168/.

Grasselli, Chiara. "Multi-domain orchestration of virtualized mobile core networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

Shinde, Swapnil Sadashiv. "Radio Access Network Function Placement Algorithms in an Edge Computing Enabled C-RAN with Heterogeneous Slices Demands." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20063/.

Mouawad, Nadia. "SDN based Mobility Management and Quality of Service Provisioning for 5G Vehicular Networks." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASV003.

Mayoral, López de Lerma Arturo. "Integrated IT and SDN Orchestration of multi-domain multi-layer transport networks." Doctoral thesis, Universitat Politècnica de Catalunya, 2019. http://hdl.handle.net/10803/667694.

Schmidt, Robert. "Slicing in heterogeneous software-defined radio access networks." Thesis, Sorbonne université, 2021. http://www.theses.fr/2021SORUS525.

Wang, Yong-Hsiang, and 王永翔. "Towards Modularization/Virtualization/Slicing of 5G Network Functions." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/qfum3r.

Cruz, Henrique Almeida Andrade de Castro. "5G network slice manager." Master's thesis, 2019. http://hdl.handle.net/10773/29573.

Fonseca, João Pedro Celestino da. "5G interdomain orchestration mechanisms for flexible vertical services deployment." Master's thesis, 2021. http://hdl.handle.net/10773/32336.

Wu, Ya-Syuan, and 吳亞璇. "Slicing Network Function Orchestration and Resource Management Optimization in 5G C-RANs." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/bh85rr.

Ferreira, Diogo Daniel Soares. "Control and management mechanisms in 5G networks: operator networks." Master's thesis, 2019. http://hdl.handle.net/10773/29643.

Martiradonna, Sergio. "Simulation Models and Advanced Management Techniques for 5G & Beyond Radio Access Networks." Doctoral thesis, 2022. http://hdl.handle.net/11589/232750.

WU, ZHI-XIAN, and 伍志賢. "Adaptive Optimal Network Slicing and Service Function Chaining of NFV/SDN Controls for Diverse Services Accessing in 5G with MEC and Global Cloud Computing." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/njyy3p.