research paper on wireless communication pdf

Evolution and Impact of Wi-Fi Technology and Applications: A Historical Perspective

• Published: 19 November 2020
• Volume 28 , pages 3–19, ( 2021 )

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• Kaveh Pahlavan 1 &
• Prashant Krishnamurthy 2  

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The IEEE 802.11 standard for wireless local area networking (WLAN), commercially known as Wi-Fi, has become a necessity in our day-to-day life. Over a billion Wi-Fi access points connect close to hundred billion of IoT devices, smart phones, tablets, laptops, desktops, smart TVs, video cameras, monitors, printers, and other consumer devices to the Internet to enable millions of applications to reach everyone, everywhere. The evolution of Wi-Fi technology also resulted in the first commercial piloting of spread spectrum, high speed optical communications, OFDM, MIMO and mmWave pulse transmission technologies, which then became more broadly adopted by cellular phone and wireless sensor networking industries. The popularity and widespread Wi-Fi deployment in indoor areas further motivated innovation in opportunistic cyberspace applications that exploit the ubiquitous Wi-Fi signals. The RF signal radiated from Wi-Fi access points creates an “RF cloud” accessible to any Wi-Fi equipped device hosting or supporting these opportunistic applications. Wi-Fi positioning and location intelligence were the first popular opportunistic applications of Wi-Fi’s RF cloud. Today, researchers are investigating opportunistic applications of Wi-Fi signals for gesture and motion detection as well as authentication and security. This paper provides a holistic overview of the evolution of Wi-Fi technology and its applications as the authors experienced it in the last few decades.

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1 Introduction

In the last few decades, as we were witnessing the emergence of the “information age” and the third industrial revolution, wireless access and localization played an undisputable role by enabling millions of innovative and popular cyberspace applications to connect to the Internet by anyone, anywhere Footnote 1 . These cyberspace applications have had and continue to make a fundamental impact on the way we live, conduct business, shop, access news media, deliver education, transport, care for health, and interact with the world. Today, smart phones, tablets and laptops use wireless technology to support untethered access to information, which is the most essential part of the way we live and work. Footnote 2 Smart cities monitor the environment and cyberspace intelligence is helping us as a society to optimize the way our intelligence contributes to the collective intelligence of humanity, to optimize the efficiency of consuming resources while sustaining life on the earth. The backbone of this third industrial revolution has been the computer and communication industry. The exponential growth in computational speed and the size of memory for information processing and storage has enabled implementation of numerous cyberspace applications that at the same time demand high speed communications and networking of devices, especially with tetherless connections to easily reach the numerous types of consumer devices emerging with the growth of the microelectronics industry at a reasonable cost. Wireless technologies have played an extremely important role in enabling this revolution to take place and to facilitate the access and intelligent processing of information to anyone, anytime, and anywhere.

At the time of this writing we have two different wireless data interfaces to connect a smartphone to the Internet, IEEE 802.11 wireless local area networks, commercially known as Wi-Fi, and cellular mobile data networks. Wi-Fi is the primary choice for smartphones because it can provide a higher data rate and more reliable indoor connections at a lower cost - users typically resort to cellular networks as a second choice. Researchers in next generations of cellular industry believe that as the cellular data rates and cellular costs goes down, the balance may shift to cellular networks because it is available everywhere. However, as of today Wi-Fi is the fastest and most cost-effective way of wireless Internet connectivity, especially in large parts of the world where broadband wired connectivity exists, but the latest 5G cellular technology does not. In addition to smartphones, many other devices like home entertainment systems, environmental monitoring sensors, and security systems connect to the Internet with Wi-Fi, but not necessarily through cellular networks. Wi-Fi brings people, processes, data, and devices, together and turns data into valuable information that makes life better and business thrive [ 1 ]. Some companies engaged in Wi-Fi related business resort to artistic illustrations similar to Fig.  1 (adapted from [ 2 , 3 ]) to relate Wi-Fi to human basic needs using Maslow’s hierarchy of human needs [ 4 ], with an additional lowest layer called Wi-Fi. Usually, Maslow’s hierarchy is shown as a pyramid, but to illustrate the crucial importance of Wi-Fi, in Fig.  1 the hierarchy is shown using the inverted version of the common symbol for Wi-Fi signal strength with Wi-Fi as the “most basic” of human needs.

adapted from [ 2 , 3 ]

Maslow’s hierarchy of human needs with an additional layer referring to Wi-Fi as enabler of these needs

Innovations after the first and second industrial revolution, such as the steam engine, the internal combustion engine, electricity, the telegraph and the telephone, radio, television, airplane, and rockets, had profound impacts on the way we live and have affected many other industries (such as entertainment). However, the Internet, the fruit of the third industrial revolution, enabling the emergence of the “information age”, has had a wider impact on our daily lives. The Internet provides access to unlimited amounts of information in an almost instant manner, anywhere, and that is further enhanced by wireless technologies by allowing devices to be anywhere. Indeed, Wi-Fi is the most popular of the wireless technologies to connect the devices and carry the internet protocol (IP) traffic.

As mentioned above, Wi-Fi is one of two primary wireless technologies that carries IP traffic. The IP traffic includes text, voice, images, and videos that comprises the communication needs in our daily lives and it is a good measure of information exchange on the Internet. A reliable source for measurement and prediction of IP traffic is the Cisco Visual Networking Index: Global Mobile Data Traffic Forecast [ 5 ]. Figure  2 , adapted from this source, shows the breakdown of this data from Mobile, Wi-Fi and Fixed access in different years. We use their prediction of traffic in 2022 as a measure to demonstrate the role of Wi-Fi in handling IP traffic. The traffic is divided into wireless (Wi-Fi and cellular mobile) and wired (Ethernet) with wireless carrying 70.6% and wired carrying 29.4%. Because of its flexibility of connection, being available anywhere, wireless traffic is more than twice the wired traffic. Fixed devices generate 58% of the traffic and mobile devices generate 42%. Wi-Fi carries 22.9% of the traffic from mobile devices (that also have a cellular connection) and 28.1% of traffic from Wi-Fi only devices for a total 51% of the entire traffic. This means that by the year 2022, Wi-Fi may carry the majority of IP global traffic soaring to reach the unbelievable high value of zettabytes (10 21 bytes). The reason for the success of Wi-Fi over wired Ethernet, carrying 29.2% of the traffic, is Wi-Fi’s connection flexibility, and the reason for success over cellular, carrying 19.6% of traffic, is Wi-Fi’s higher speed and less expensive connection cost. We use these numbers as a proxy metric to now explain why we need Wi-Fi. This discussion clarifies our “artistic expression” in the beginning of this section, about the impact of Wi-Fi in our daily needs, in a broader context with historical and projected usage numbers.

adapted from [ 5 ]

Approximate global monthly IP traffic for different Internet access methods in 2017 and 2022 (note Exabyte is 10 18 bytes); Data

In this brief introduction we provided our view on the importance and impact of Wi-Fi technology. In the remainder of this paper we provide a holistic overview of the evolution of Wi-Fi technology and its applications. This is a huge area and it is very difficult to write a paper that includes every aspect of its history. As members of a pioneering research center in this field [ 6 ], we provide a historical perspective of evolution of Wi-Fi in the way that we experienced it in the past few decades (and the paper is driven by this personal lens). We approach this challenging task from three angles. First, how the physical (PHY) and medium access control (MAC) for wireless communications with Wi-Fi technology evolved and what were the novel wireless transmission technologies that were introduced in this endeavor. Second, how Wi-Fi positioning emerged as the most popular positioning technology in indoor and urban areas and how it has impacted our daily lives. Third, how other cyberspace applications, such as motion and gesture detection as well as authentication and security, are emerging to revolutionize human computer interfacing with the RF cloud of Wi-Fi devices.

2 Evolution of Wi-Fi Communications Technology

In this section we first discuss the origins of the PHY and MAC layer technologies for Wi-Fi by separating their origins into three eras: (i) prior to 1985, when the pioneering technologies for WLAN were invented, (ii) during the period 1985–1997, when IEEE 802.11 and Wi-Fi technology became IEEE standards and finally, (iii) from 1997 to the present, when orthogonal frequency division multiplexing (OFDM) and multiple-input multiple-output (MIMO) technologies enabled Wi-Fi to enhance the supported data rates from 2 Mbps to Gbps. Then we discuss the evolution of the market and the applications of Wi-Fi that eventually emerged.

2.1 The Origin of WLAN Technologies Before 1985

Around the year 1980, IBM‘s Rueschlikon Laboratory, Zurich, Switzerland began research and development on using InfraRed (IR) technology to design WLANs for manufacturing floors [ 7 ]. At that time, wired local area networks (LAN)s were popular in office areas and large manufacturers such as General Motors (GM) were considering their use in computerized manufacturing floors. To wire the inside of offices, it was necessary to snake wires in walls and was easier with low-height suspended ceilings. In manufacturing floors, there are limited numbers of partitioning walls and moreover ceilings are high and made of hard material. Consequently, WLAN technology offered itself as a practical alternative for manufacturing floors. Around the same time frame, HP Palo Alto Laboratory, California, reported a prototype WLAN using direct sequence spread spectrum (DSSS) with surface acoustic wave (SAW) devices (for implementation of a matched filter at the receiver) [ 8 ]. HP Laboratories at that time had open space offices without partitioning by walls, which again created challenges for wiring through the walls. Dropping wires from the ceiling to desktops was not aesthetically pleasing. It was in this time that optical wireless and spread spectrum, combined with spread spectrum technology emerged, that could support huge amounts of capacity for indoor WLANs to connect desktops and printers together in a local network [ 9 , 10 ].

Prior to all this, Norm Abramson at University of Hawaii had designed the first experimental wireless data network, the ALOHA system [ 11 ]. The difference between the approach taken by IBM or HP and this approach was that ALOHA was an academic experimentation of wireless packet data networks with an antenna deployed outdoors with relatively low data rate modems at a speed of around 9600 bps. However, the concept had inspired low speed wireless data networking technologies such as Motorola’s ARDIS, and Ericsson’s Mobitex, (which we refer to as mobile data services now generally subsumed by cellular data services in 3G, 4G, and beyond). For WLAN technologies, the antenna is installed indoors, and the data rate needed to be at least 1 Mbps (at that time) to be considered by IEEE 802 standards organization community as a LAN [ 12 ]. The medium access control of both wireless data services was contention based, originally experimented in ALOHA, and later on evolving into carrier-sensing or listen before talk based contention access adopted by wired LANs by the IEEE 802.3 standard, commercially known as the Ethernet with some variation.

The main obstacle for commercial implementation of the early WLANs were interference and availability of a low cost wideband spectrum in which the WLAN could operate. Indoor optical WLANs did not need to consider regulation by the Federal Communications Commission (FCC) and they could potentially provide extremely wide bandwidths. However, optical communications cannot penetrate walls or other obstacles and thus, the operation becomes restricted to open areas, which are often small inside buildings. Spread spectrum was an anti-interference technology, which at that time could potentially manage the interference problem allowing multiple users to share a wideband spectrum [ 9 , 10 , 13 ]. In the summer of 1985, Mike Marcus, the chief engineer at FCC at that time, released the unlicensed Industrial, Scientific, and Medical (ISM) bands with restrictions of having to use spread spectrum technology for interference management [ 14 ]. For WLANs to become a commercial product, there was need for large bandwidths (at that time) and modem technologies that could overcome the challenges of indoor RF multipath propagation to achieve data rates beyond 1 Mbps required to be considered by the IEEE 802 committee as a LAN. The ISM bands and spread spectrum technology could address both issues.

2.2 Evolution of WLAN Technologies and Standards Between 1985 and 1997

The summer of 1985 was a turning point in the entrepreneurship for implementation in the WLAN industry. The FCC had released the ISM bands for commercial implementation of low power spread spectrum technology in May [ 14 ]. The suitability of RF spread spectrum and IR for implementing wireless office information networks had captured the cover page of the IEEE Communication Magazine in June [ 10 ]. Suddenly, several startup companies and a few groups in large companies, almost exclusively in North America, emerged to begin developing WLANs using spread spectrum and infrared technologies. Infrared devices did not need FCC regulations because they operate above the 300 GHz frequency, the highest governed by this agency. Among the exceptions for locations of these companies, was a small group in NCR, Netherlands, which designed the first Direct Sequence Spread Spectrum (DSSS) technology to achieve 2 Mbps [ 15 , 16 ]. Other companies, such as Proxim, Mountain View, CA, resorted to Frequency Hopping Spread Spectrum (FHSS), and a third group led by Photonics, supported by Apple, resorted to an IR solution for WLANs. All three groups could achieve 2 Mbps. These three groups, originally started in the late 1980’s, laid the foundation for the first legacy IEEE 802.11 standards series, finalized in 1997. The final standard for DSSS and FHSS operated in the 2.4 GHz ISM bands. Other exceptions in technologies were efforts by Motorola, IL and WINDATA, Marlborough, MA. Motorola introduced a revolutionary WLAN technology operating in the 18 GHz licensed bands achieving 10 Mbps using a six sectored antenna configuration [ 17 , 18 ], and WINDATA, achieved 6 Mbps in a dual band mode with 2.4 GHz for uplink and 5.2 GHz for downlink. The work presented in [ 17 , 18 ] was the first time a wireless network was adopting frequencies in 18 GHz with directional antennas. We may refer to this work as the first attempt to use close to mmWave technology for modern wireless networking, which is now adopted by 5G cellular networks [ 19 ]. However, mmWaves cannot penetrate walls well, which restricts their coverage in indoor areas. This restriction is less of a concern in cellular networks with outdoor antenna deployments.

The first academic research in the physical layer of WLANs began at the Worcester Polytechnic Institute, Worcester, MA in Fall of 1985 [ 6 ]. The early academic research literature in this area began with the empirical modeling of the multipath radio propagation in indoor areas [ 20 , 21 , 22 , 23 , 24 , 25 ], examining decision feedback equalization (DFE) [ 26 ], and M-ary orthogonal coding [ 27 ] to achieve data rates beyond the 2 Mbps rates studied by the IEEE 802.11, to achieve rates on the orders of 20 Mbps, and the integration of voice and data for WLAN [ 28 ]. A form of M-ary orthogonal signaling was adopted by IEEE802.11b standard, DFE was adopted by the Pan European HIgh PERformance LAN (HIPERLAN)-I standard, and a form of OFDM was implemented in HIPERLAN-2 and IEEE 802.11a standards. A breakthrough, patented in this era, was the application of OFDM to WLANs, first filed by the Commonwealth Scientific and Industrial Research Organization (CSIRO), Sydney, Australia [ 29 ]. The origins of equalization, quadrature amplitude modulation (QAM), and OFDM transmission technologies were first implemented for commercial voice band communications [ 30 ]. The use of DFE was first adopted for wireless data communications over multipath troposcatter channels in military applications [ 31 ] and M-ary orthogonal coding was an extension to Code Division Multiple Access (CDMA) to increase the capacity for military applications [ 32 ]. The novelty of these technologies in this later time was in their application in commercial WLANs in non-wired and non-military applications.

The IEEE group for WLAN standardization was first formed as IEEE 802.4L in 1998. The IEEE 802.4 group was devoted to Token Bus LANs for manufacturing environments and was on the verge of disbanding. The rationale for introducing WLANs in this group was that new IEEE standards usually begin in a closely related standard and after going through the establishment procedure, may form their own group and standard series. The IEEE 802.11 group was the same group from IEEE 802.4L formed later in July 1990 [ 33 ]. In the early days of this standard the important challenge was to find the correct direction for the future technology among a number of acceptable technologies. In 1991, the standards group participated in the first IEEE sponsored conference to address this issue [ 34 ]. The early IEEE standards for wired LANs were differentiating from each other through their MAC method. IEEE 802.11 was the first with three MAC mechanisms (that could work together) and three PHY layer methods. The legacy IEEE 802.11 standard was completed in 1997 with three PHY layer recommendations, DSSS and FHSS operating in the 2.4 GHz ISM bands and Diffused Infrared (DFIR) wireless optical options. All three PHY layer options operated at raw data rate options of 1 and 2 Mbps and employed three possibilities for the MAC: carrier sensing - CSMA, request and clear to send - RTS-CTS, and polling (point-coordination-function) - PCF. RTS/CTS and PCF were designed to operate in conjunction with the base CSMA with collision avoidance (CSMA/CA).

The HIPERLAN was another WLAN standardization activity, sponsored by the European Telecommunications Standards Institute (ETSI), which began its work in 1992. The HIPERLAN-1 standard was the first attempt to achieve data rates above 10Mbps using DFE technology and in the 5 GHz unlicensed bands [ 35 ]. This standard was also completed in 1997, but it failed in developing a market for itself. Another more popular but extensive standardization activity for wireless indoor networking in this era was Wireless Asynchronous Transfer Mode (W-ATM), which aimed to integrate local wireless traffic with an ATM backbone wired technology [ 36 , 37 ]. A comparison of this technology with Wi-Fi is available in [ 38 ].

In summary, it is fair to say that during the 1985–1997 era, the WLAN industry was in the process of discovering technologies for wideband indoor wireless communications and it examined spread spectrum, M-ary orthogonal coding, IR, licensed bands at 18 GHz with directional antennas, DFE, and OFDM technologies and the importance of the analysis of the effects of multipath and appropriate mitigation techniques to achieve higher data rates. The spread spectrum and Infrared technologies of the legacy IEEE 802.11 standard were the only technologies which survived in the market and a modified form of these technologies have remained in other popular standards such as Bluetooth, using FHSS and ZigBee using DSSS. M-ary orthogonal coding and OFDM appeared in later standards. This era also opened channels for dissemination of research and scholarship through publication channels. The first IEEE workshop on WLAN (1991), and the IEEE International Symposium on Personal, Indoor, and Mobile Communications (1992), hosted first panel discussions on the future of the WLAN industry in cooperation with the IEEE 802.11 standardization organization. The year 1994 marked the establishment of the first scientific journal, the International Journal of Wireless Information Networks, and the first scientific magazine related to this subject, IEEE Personal Communications, which later changed its name to IEEE Wireless Communications. The pioneering textbooks in Wireless Information Networks [ 39 ] and Wireless Communications [ 40 ] also emerged in this era.

2.3 Evolution of WLAN Technologies and Standards After 1997

The IEEE 802 standards define MAC and PHY specifications of local networks as standards for vendors to be able to interoperate. Figure  3 shows the evolution of the PHY and MAC layers of the IEEE 802.11 standards from the beginning to the present. The first step of evolution of the standard after completion of the legacy 802.11 standard in 1997, was the IEEE 802.11b standard that used complex M-Ary orthogonal coding known as Complementary Code Keying (CCK). The IEEE 802.11b standard was completed in 1999. Devices using this standard operated at speeds up to 11 Mbps with a fall back to 1–2 Mbps using the legacy 802.11 standard. Both IEEE 802.11b and the legacy IEEE 802.11 devices operated in the 2.4 GHz bands. In 1999, the IEEE standards body also completed specifications for IEEE 802.11a operating at 5.2 GHz using OFDM transmission technology to achieve data rates up to 54 Mbps. The IEEE 802.11a PHY layer was coordinated with the efforts in the HIPERLAN-2 standard in Europe [ 41 ]. In comparison with Wi-Fi, the centralized MAC of HIPERLAN-2 [ 42 ] was expected to allow better management of quality of service, vital to the cellular telephone industry. Perhaps that was the motivation of Ericsson to pursue the leadership of this effort. However, in a manner similar to wireless ATM, this standard did not achieve commercial success. This could be because in wide area networking we have large number of users with less bandwidth resources and rationing this scarce resource requires centralized supervision by enforcing quality of service rules. In local areas with abundant availability of bandwidth and only a few users, a distributed MAC would be more practical in that time frame. Although a new standard for integration of local and metropolitan area networks, such as HIPERLAN-2 or wireless ATM, did not became a reality, the need for this integration of cellular networks with Wi-Fi, became a reality. The concept of integration of Wi-Fi with cellular using vertical hand-offs and mobile IP technology emerged in the early days of commercial popularity of Wi-Fi [ 43 ] and it has prevailed all the way along up to the time of this writing but this time with operating system and software control. The continuation of the ideal of new standards for local operation continued into Femtocells [ 44 ] and long-term evolution-unlicensed (LTE-U) [ 45 ] – operation of 4G cellular networks in the unlicensed spectrum, but neither has created any serious challenge to the Wi-Fi market yet. In the same way that the WLAN industry in its early days of survival resorted to point-to-multipoint outdoor installation for wider area coverage, it can be thought that Wi-Max [ 46 ] emerged as a successful application of local area centralized medium access control technologies for outdoor antenna deployments. The wireless ATM, HIPERLAN, Femto-cell, LTE-U and Wi-MAX technologies created a significant hype in scientific publication venues and among national funding agencies, but they failed to keep investors in developing these technologies as happy as those that invested in Wi-Fi. Later, in 2003, the IEEE 802.11g working group defined OFDM operation in 2.4 GHz with the same data rates as IEEE 802.11a, which expanded the horizon for Wi-Fi market.

Evolution of Wi-Fi technologies and standards

The breakthrough in wireless communications at the turn of the twenty first century was the discovery of multiple antennae streaming benefitting from space time coding (STC) and MIMO technology. The foundation of multiple antenna streaming is based on two technologies: adaptive antenna arrays to focus the beam pattern of antennas and space time coding (STC) which is a coding technique enabling separation of multiple streams of data with coding. The benefits of multiple transmitting and receiving antennas existed in the antenna and propagation society literature since the 1930’s [ 47 ]. Seminal work on STC [ 48 , 49 , 50 ] enabled multiple streams of data and that is why it is considered as one of the most important worldwide innovations around the turn of the twenty first century. Multiple streams of data using MIMO technology in conjunction with OFDM and benefitting from STC opened a new horizon in scaling the physical layer transmission rates in multipath fading channels [ 51 ]. The next giant step in the evolution of technology for the IEEE 802.11 community was the introduction of IEEE 802.11n in 2009, using MIMO technology to enable multiple data streams to achieve raw data rates up to 600 Mbps both in the 2.4 and 5.2 GHz bands. Other standards such as IEEE 802.11ac and 802.11ax, followed the same OFDM/MIMO technology.

Another major hype in physical layer technologies for wireless communications was mmWave pulse transmission technology. The IEEE 802.11ad group adopted mmWave pulse transmission technology in the 60 GHz band with utra-wideband (UWB) transmission bandwidth exceeding 2 GHz to achieve data rates on the order of Gbps. Although mmWave technology became an important part of the 5G cellular networking industry [ 19 ], IEEE 802.11ad and 802.11ay, as the first completed standards using these technologies have not been successful in attracting a huge share of the WLAN market. As we explained earlier, mmWaves in indoor areas has coverage restriction that does not apply to outdoor antenna deployments.

Regarding the MAC of the IEEE 802.11, the main two techniques which became dominant were CSMA/CA and request/clear to send - RTS/CLS. Carrier sensing with collision avoidance—CSMA/CA was a practical extension to the wireless medium of CSMA/CD (collision detection), which was adopted for the IEEE 802.3 standard, commercially known as Ethernet. The IEEE 802.11 devices grew with the name “wireless Ethernet” and CSMA/CA would enable Ethernet to finally have a wireless extension. The RTS/CTS mechanism was originally designed to address the hidden terminal problem, but it became more popular for application with directional antennas for IEEE 802.11ac, ad. An analytical comparison of these two MAC techniques is a challenging problem that received a very thorough and popular analysis in the year 2000 [ 52 ].

A good survey of all these standards is presented at Wikipedia [ 53 ]. Here, we argue that all major PHY layer technologies evolved for wireless information networks: optical wireless, spread spectrum, M-ary orthogonal coding, OFDM, MIMO, and mmWave technologies were first adopted by the IEEE 802.11 standardization community. Then, DSSS and orthogonal signaling in 2G/3G, OFDM/MIMO in 4G, and mmWave in 5G/6G cellular telephone technologies, came after the adoption of these technologies in IEEE802.11 standards. The MAC of cellular telephone industry is centralized and different from that of WLANs, primarily to accommodate high traffic densities and support higher level of mobility for users with metropolitain area coverage. The IEEE 802.15 wireless personal area networks followed a similar pattern by adoption of FHSS for Bluetooth and DSSS for ZigBee, after they were first introduced by the IEEE 802.11 standard. The MAC of Bluetooth and in particular ZigBee carry similarities with those of the MAC of IEEE 802.11. Therefore, it is fair to say that the WLAN industry pioneered the design of the dominant PHY technologies of today’s wireless networking industry and this is a huge technological impact in the communication of humans, devices, and machines.

2.4 Evolution of Wi-Fi Applications and Market

Applications fuel the market, and they are intimately linked to the network through devices running these applications. Local area networks were networking computers to share common peripheral devices such as printers or storage memories and later machines in a manufacturing floor. In the late 1980’s and early 1990’s, when the WLAN industry began to test the market, Personal Computers (PC) and Workstations were competing to capture the market of mini-computers. Laptops became popular in this market a little later. From the networking point of view in that era, engineers were searching for wiring solutions for the growing market of these devices to connect them with minimal effort. The early WLAN startups were thinking of wireless as a replacement to wired LANs to connect PC’s in open areas such as manufacturing floors or open offices without partitions. These companies were assuming that these small computers will grow on office desks or on manufacturing areas in clusters. The idea was that if we connect this cluster of desktop computers to a hub and then we connect the hub to a central node connected to the Ethernet backbone, we will avoid snaking of wires or wires hanging from the ceilings of manufacturing floors and offices. In a typical startup proposal for venture capital, these companies were arguing that close to half of the cost of the LAN industry was associated with installation and maintenance of these networks, which can be vanishingly small when we use wireless technology. As a result, the first WLAN products were shoe box sized hubs and central units and following the above argument, these companies were estimating that a market of a few billion-dollars would emerge for these devices in early 1990’s. Based on this idea a typical startup company or a small group in a large company could raise up to $20 M at that time, adequate to support a design and marketing team to get the product going. Therefore, the early products from NCR, Proxim, Aironet, WINDATA, Motorola, NCR, Persoft, Photnics and others appeared in the market (see Fig.  4 a for samples of these products). The reader can find a variety of photos of these historical WLAN products in the proceedings of the first IEEE Workshop on WLAN [ 34 ]. This workshop was held in Worcester, MA, in parallel to the IEEE 802.11 official meeting to decide on the future of this industry. Around the year 1993 these products were in the market but the expected few billion-dollar market developed only to a few hundred million dollars. These sales were mostly for selected vertical applications and by research laboratories discovering the technology, not for the horizontal market for connecting desktop computers everywhere. This resulted in a retreat in the original few tens of companies, searching for a new market domain.

a Some historical pioneering shoe box size WLANS designed by Motorola, Persoft, Aironet, and WINDATA, b the wireless PC cards and its access points in Roamabout designed by Digital Equipment Corporation

During the market crash of 1993 for the WLAN industry designed for connecting clusters of desktop computers, two new applications emerged. The first solution was point-to-point or point-to-multipoint WLAN bridges. The idea was to allow WLANs to operate outdoors and add a strong roof top antenna to take advantage of free space propagation and antenna gains to extend the expected 100 m indoor coverage to outdoor coverage spanning a few miles. As examples of these markets, two hospitals in Worcester, MA, which were a few miles away could connect their networks with low cost private WLANs, instead of using expensive leased lines from telephone companies. Or, Worcester Polytechnic Institute could connect the dormitories to the local area network of the main campus. The other idea was to design smaller wireless PC Cards for the emerging laptop market. Figure  4 b shows the picture of the Roamabout access point box and the laptop wireless PC Cards of the first successful product of that type, designed at Digital Equipment Corporation, Maynard, MA. These devices were the showcase of the second IEEE Workshop on WLANs, October 1996 [ 54 ]. Examples of a practical market for laptop operation included large financing corporations such as Fidelity in Boston, who would purchase laptops for their marketing, sales, and other staff. Such companies wanted their staff to be connected to the corporate network, when in office.

The next wave of market demand for WLANs was for small office/ home office (SOHO) application, which began around 2000. The authors believe that this story began in the mid-1990 with the penetration of the Internet to homes with service providers like America online (AOL) for small indoor area distribution of signals. The penetration of the Internet in homes fueled the development of cable modems and digital subscriber line (DSL) modems for high data rate home services and with that came the growth in the number of home devices and demand for Home-LAN technology. Several ideas such as using home wiring or electricity wiring for implementation of Home-LANs were studied, but Wi-Fi emerged as the natural solution. At that time, the price of a Wi-Fi access point (AP), such as the one made by Linksys, had fallen to below $100 and wireless PC Cards could be purchased with a reasonable price of a few tens of dollars. The original early shoe boxes had been selling for a few hundred dollars for the hub and up to a few thousand dollars for the AP! With these lower prices, coffeeshops and other small businesses could afford to provide free Wi-Fi and homeowners could bring Wi-Fi home. This was perhaps the first large market bringing Wi-Fi from office to the home. During the 2000’s despite the crash of the .com industry, the Wi-Fi market in this domain began to grow exponentially. The exponential growth of Wi-Fi for SOHO encouraged consumer product manufacturing to consider Wi-Fi for integration in their products (e.g., in digital cameras and TV monitors). This market was however not that large, and the ease of Wi-Fi networking did not exist. The integration of Wi-Fi in the iPhone was the next major marketing break-through for Wi-Fi popularity and market growth. Integration of Wi-Fi into smartphone increased the sale of Wi-Fi chipsets to billions and further enabled Wi-Fi based positioning. More recently Wi-Fi applications expanded by emergence of motion and gesture detection as well as authentication and security with Wi-Fi signals to facilitate human computer interaction. Figure  5 summarizes the evolution of Wi-Fi applications. We provide an overview of these cyberspace application using the Wi-Fi signal in the remainder of this paper.

Evolution of Wi-Fi applications and market

3 From Wi-Fi to Wi-Fi Positioning – Emergence of Another “Killer App”

In the early 1990’s, when the expected market for WLANs did not emerge, those invested in this emerging technology began to discover reasons for the lack of success. These were the CEO’s of startups and managers of the WLAN projects in larger companies. Some were associating the lack of success to the delay in finalizing the IEEE 802.11 standard and some to the lack of a “Killer App”. The standard itself was completed in 1997 (with interoperability tests) and soon, deployment in SOHO scenarios appeared as the “Killer App” in the late 1990’s. Adoption of Wi-Fi in smart phones in the late 2000 s was another breakthrough “Killer App” of Wi-Fi technology, which enabled these devices to execute a number of user applications such as integration of search engines, email, and large file transfers using smart phones. Other networked applications that were typically done on a desktop using the Internet followed (e.g., e-commerce and banking). However, the Wi-Fi Positioning engine was perhaps the most innovative “Killer App” related to Wi-Fi technology that was introduced by the iPhone. When Steve Jobs introduced Skyhook of Boston’s Wi-Fi positioning technology in the iPhone, and he called it “Cool” and a “neat idea” [ 55 ], because it was different.

3.1 The Origin of Wi-Fi Positioning

Because of the commercial success of the Global Positioning System (GPS) in the mid-1990’s, the fact that GPS does not work properly in indoor areas, and the FCC mandate on E911 services for cellular networks, the indoor geolocation science and technology began to emerge in the late 1990’s [ 56 , 57 ]. The expensive cost of dense infrastructure needed for commercial positioning applications led that industry to resort to opportunistic positioning. Opportunistic Wi-Fi positioning using received Wi-Fi signals radiated from the Wi-Fi access points, originally deployed for wireless communications in office areas, was the first idea to attract attention for a cost effective indoor geolocation system [ 58 , 59 ]. The received signal strength (RSS) or time of arrival (TOA) of the signals radiated from the access points could be used for positioning since the locations of the access points were known and could be used for this purpose. The RSS was a quantity that was easier to measure but it was not accurate enough for good location granularity. The use of RSS for positioning became practical only by incorporating intelligence through training the system with fingerprinting and using pattern recognition algorithms to find the location [ 60 , 61 ]. This training was done with similar devices that collected data at known locations to make up the training fingerprints. The more accurate TOA measurements [ 59 ] needed additional design to be incorporated through a TOA acquisition system. The widespread deployment of Wi-Fi in office areas was more fertile for commercial development and a few companies, such as Ekahau, Helsinki, Finland, adopted that technology as their Wi-Fi positioning indoor geolocation system. Today this Wi-Fi positioning industry is sometime referred to as “real time positioning system” RTLS) [ 62 ]. The commercial success of RTLS was rather limited and it never generated a substantial market for Wi-Fi positioning.

Another approach to Wi-Fi positioning was to collect the fingerprint of locations from the APs from a vehicle driving in the streets and tagging the fingerprints with the GPS location reports of the vehicle at the time of measurement. This was the approach used by Skyhook Wireless, Boston, MA, which was adopted by the iPhone and was trademarked as “Wi-Fi Positioning System” (WPS) by Skyhook [ 62 ]. The difference between RTLS and WPS are: (1) RTLS typically covers only one building while WPS covers a metropolitan area, (2) fingerprinting in RTLS for a given area of coverage is much more expensive than WPS, (3) RTLS provides an accuracy of around a few meters while WPS provides for accuracies on the order of 10-15 m. Larger coverage areas with accuracies of 10-15 m enabled turn-by-turn direction finding for vehicles in the metropolitan areas that was the highlight of positioning applications in the original iPhone [ 55 ]. As a result, WPS became a commercial success and a highlight of the magic of iPhone applications. Today, Skyhook’s database contains over a billion AP locations worldwide and its database receives over a billion hits per day from smart device applications using WPS technologies. Google, Apple and other cyberspace giants have formed their own WPS system with their own database of APs along with that of the Skyhook.

3.2 Emergence of Location Intelligence from WPS

WPS is a device-based positioning system, i.e., a device reads the RSS of surrounding Wi-Fi devices with their MAC addresses. These readings are transferred to a central server with a database of the mapping of fingerprints to position and the system can determine the location of the device using the fingerprint database. Thus, communication link to carry these information between the device and the server is essential here, which Wi-Fi already provides. This process traditionally takes place in two steps, fingerprinting and positioning. During the fingerprinting phase, a data acquisition device located in a vehicle drives on the streets. In this phase the MAC addresses of the APs and their associated RSS are sampled approximately every second and each sample is tagged with the GPS reading of the location at the time of sampling. The fingerprint in the database, the MAC addresses and the GPS readings are post processed with proprietary algorithms to associate the MAC addresses of each AP to a location based on GPS readings. In this way, the WPS system builds a database of locations of the APs in the areas that the vehicles drive through. When a device, not knowing its location, sends the MAC address of an AP and RSS readings to the server, the server uses another proprietary algorithm to position the device and determine its location. The major WPS service providers, Skyhook, Google, and Apple, each receive approximately one billion requests per day from millions of devices. The one billion hits each associate a personal device address to a location and one can track the movements of the device. Applications drawing from this motion tracking capability of WPS are referred to as “location intelligent” and are said to be providing location-based services. One simple location intelligence application is the location-time traffic analysis. We may grade the density of hits per-hour of the day to determine where the people are going, and smart marketing strategies can benefit from that data. Other location intelligent applications include “geofencing” of elderly people, animals, prisoners, suspicious people, real world consumer behavior, location certification for security, positioning IP addresses, and customizing contents and experiences. During the recent COVID pandemic, Apple made its mobility data (when people were asking directions – and thus location information) public to enable assessing the social distancing and quarantine postures in various cities and communities [ 63 ]. Of course, this data also includes cell phone technology, but it is an indication of how far sensing signals from mobile devices has come, starting with Wi-Fi positioning.

The future directions in Wi-Fi positioning is in the integration of RSS signals with other sensor readings on smart devices (accelerometers for instance) to enhance the precision and flexibility of positioning. There are research works on integration of mechanical sensors such as accelerometer and gyroscope on robotic platforms with Wi-Fi positioning [ 64 ], there are works in integrating more precise UWB positioning with limited coverage with wider coverage Wi-Fi positioning [ 65 ], and frameworks for generalizing fingerprinting in multi-sensor environment [ 66 , 67 ]. Other researchers investigate submeter Wi-Fi positioning using Wi-Fi channel state information (CSI) [ 68 ].

4 Wi-Fi and Emerging Cyberspace Applications

Wi-Fi localization, either for local indoor areas (what we referred to above as RTLS) or for metropolitan areas (which we referred to as WPS) makes use of the RSS feature from APs that are broadcasting signals, by reading their broadcast quasi-periodic beacon signal. Beacons are used to advertise the availability of an AP thereby enabling other devices like smartphones or laptops to access the Wi-Fi network (called basic or extended service area in the standard). A device that reads the beacon only for localization does not need to connect to the AP because it only needs the MAC address and the RSS information for positioning itself. Access Points radiate a radio frequency (RF) cloud around themselves which are available to any device in their area of coverage. The RSS is one feature of the Wi-Fi RF cloud, which can be measured easily without any coordination between the transmitter and the receiver. With some coordination, the receiver can measure the Time of Arrival (TOA) of a signal as well. Today, the dominant transmission technique in Wi-Fi is MIMO-OFDM. Devices which can use MIMO_OFDM can also measure Direction of Arrival (DOA), channel impulse response (CIR), and the Channel State Information (CSI) of the multipath medium between the transmitter and the received [ 69 ]. These features (as well as the RSS feature) vary statistically depending on the multipath characteristics arising from the motion in the environment. Consequently, it is possible to use these features for motion and gesture detection. In this section, we begin by describing these statistical behaviors and following that we briefly review the emerging research benefitting from analyzing these statistical changes in features, toward the design of cyberspace applications for human-computer interactions.

4.1 Characteristics of Features of Wi-Fi Signals in Multipath

Figure  6 , illustrates a general line-of-sight (LOS) scenario for a MIMO-OFDM Wi-Fi communication with typical multipath propagation. Multiple paths are reflected from walls and other stationary and moving objects in the environment. These paths are often clustered due to scattering from smaller objects located close to each other. In an ideal situation, the stationary baseband CIR for wireless devices operating in multipath indoor areas is represented by:

where \((\alpha_{i} ;\tau_{i} ;\theta_{i} ;\psi_{i} )\,\) are the magnitude, TOA, phase, and DOA of the i-th path, and N is the number of multipath components. In this equation the phase of the arriving path and the TOA are related by \(\theta_{i} = 2\pi f_{c} \tau_{i} \,\) . Therefore, if we measure the TOA, we can calculate phase and vice versa. Since the phase is a periodic function, in calculation of the TOA from the phase we should consider such ambiguities [ 57 ]. The TOA, amplitude, and phase of each path as well as the RSS can be calculated from the length of the path, by:

, where \(f_{c} \,\) is the carrier frequency of the signal, \(\lambda = c/f_{c} \,\) is the wavelength of the signal, \(d_{i}\) , is the length of the path, and c is the speed of light. If we have an antenna array, we can calculate the DOA from TOA differences between the received signals from different array elements. When we have motion in the environment, either by moving the location of the devices or objects move in the environment, the lengths of the various paths change affecting features such as RSS, TOA, and DOA. In addition, due to Doppler shift effects, a change in the length of a path with the velocity of \(v_{i} \,\) meters per second causes a frequency off-set in the carrier frequency calculated from [ 57 ] :

Multipath scenario of RF propagation for Wi-Fi enabled indoor wireless communications using OFDM/MIMO technology

In summary, if a receiver can measure the CIR and the frequency off-set, it can monitor the length and direction of the path as well as the velocity of changes in the path lengths.

As shown in Eq. ( 2 ), the amplitude of the received signal, \(\alpha_{i}\) , from a path changes inversely with the increase in the path length, \(d_{i}\) . The phase of the arriving signal from a path, \(\theta_{i}\) , changes rapidly for a value of \(2\pi\) each time the length of the path increases by a value equivalent to the wavelength of the signal, \(\lambda\) . The rapid change in phase of the arriving paths causes fading and these rapid changes are caused by motion of the device, motion of people around the devices, and by the changes in frequency of operation. In the wireless communication literature, fading characteristics is studied under temporal, frequency-selective, and spatial fading [ 39 ].

The traditional application of measurements of the CIR is in high-speed wireless communications and in radars. Modern applications that use CIR measurements are in wireless positioning, gesture, and motion detection, and in authentication and security. Each application relies on certain specific features of the CIR and for that needs to measure those features with certain precision at the receiver. The accuracy of measurement of these features at a receiver relies on training (known signals), the bandwidth of the system, availability of antenna arrays at the receiver, and accuracy of synchronization between the transmitter and the receiver. As a result, the specific implementation of these applications have unique challenges and demand research and development and decades of years of evolution. Traditional radar and digital communication systems were built around the second World War and today we are still developing new cyberspace applications around them. What is changing is the application environment and characteristics of multipath inside those environments. As we move from open areas to sub-urban, densely populated urban areas, and indoor areas, the multipath propagation of RF signals increases, and design of applications faces new challenges.

The measurement of multipath characteristics of the channel was a very challenging problem in the early 1970’s [ 70 ]. Wideband digital communication systems evolving in this era needed the estimate of multipath arrivals to enhance their data rates. Wideband multipath channel measurement in that period would be a subject for a Ph.D. thesis [ 71 ]. Today, all wireless communication devices measure the multipath characteristics as a routine in the design of their systems.

If the bandwidth of the system is wide enough so that the width of the transmitted communication symbols, the inverse of the bandwidth, is less than the inter-arrival time of the paths, a sensitive enough receiver can isolate each path and measure the features precisely. If the bandwidth of the channel is not wide enough, a receiver can only detect a cluster of paths as one path. In wireless communications we can categorize device receivers into three categories, ultra-wideband (UWB), Footnote 3 wideband (WB), and narrowband (NB). UWB systems are capable of measuring most individual paths, WB receivers measure multipath arrivals but each path is in reality an aggregate of a cluster of paths, and NB receivers receive the signal from many paths as essentially a single path that combines all multipath arrivals (see Fig.  7 ). When a receiver detects a path that is indeed the combination of several neighboring paths, due to fast variations of the phases of the original path, the amplitude of the detected path experiences Rayleigh or Rician fading and the TOA of the detected path obviously is something very different from any of the individual paths in wireless communication applications, fading causes huge degradation of the maximum achievable data rate, and to compensate for that the research community have discovered equalization, spread spectrum, OFDM, and MIMO technologies in the past several decades [ 39 ]. The popular TOA-based location related applications measure the distance from the delay of the TOA of the direct path between the transmitters and the receiver and integration of multiple paths in a single path at the receiver causes huge errors in distance estimation (1 m error for every 3 ns error in delay).

Multipath detection in UWB and multipath clustering in WB and NB receivers

The receivers of wireless communication devices employing these technologies measure the characteristic of the communication channel and characteristics of these measurements vary, depending on the architecture and bandwidth of the system. Empirical measurements and modeling of multipath RF propagation in indoor areas in the late 1980’s, first showed that if the bandwidth exceeded 100 MHz the amplitude of multipath arrivals follows a lognormal distribution, caused by shadowing, and they do not follow the commonly assumed Rayleigh or Rician multipath fading characteristics [ 24 ]. Therefore, we may consider Wi-Fi technologies using bandwidths on that order as UWB systems that can resolve the paths. The characteristics of CIR measurements with UWB systems is that the amplitude of the paths follow a lognormal distribution that is much more stable than Rayleigh/Rician distributions, and the TOA measurements are precise for calculation of the delay of the paths. The IEEE 802.11ad devices certainly follow the UWB characteristics. The IEEE 802.11ac options with bandwidth up to 160 MHz gets close to observing UWB features. However, legacy IEEE 802.11 and the popular 802.11 a,g,n,ac,ax,af can be considered as WB systems with bandwidths of approximately 20-40 MHz (and sometimes up to 80 MHz). The IEEE 802.11 standards using OFDM have sub-carriers with a bandwidth of approximately 20 MHz/64 = 375 kHz per carrier, which is considered NB. In summary, channel measurements for NB transmissions provide for a stream of Rayleigh fading amplitudes and uniformly distributed phases. The phase measurements do not support a reliable measure of distance and WB systems provide multiple streams of NB data. The UWB systems provide for multiple streams of slow lognormally fading signals with multiple streams of phases that are beneficial for accurate measurements of the delays of the associated paths.

The most popular Wi-Fi devices at the time of this writing, IEEE802.11n and IEEE 802.11ac, use MIMO-OFDM technology with three transmitters and two receiver antennas, shown in Fig.  6 . The OFDM signal has 64 sub-carriers, using 52 of these carriers for communication data. In addition, to the magnitude and phase of the carriers they also provide the frequency off-set from the center frequency as well as six streams of magnitude and phases of the CSI data. Depending on the quality of the beam forming algorithm to sharpen the beam, the CSI data can represent a single path or a cluster of paths arriving from a direction. If it is a cluster, the amplitude samples have a Rayleigh distribution and if it is a single path the amplitudes should be more stable with a lognormal fading behavior. The number of paths in the cluster also governs the accuracy of delay of the path measurement using the phase of the received CSI stream [ 72 ]. Recently, these data streams have been paired with artificial intelligence (AI) algorithms to initiate research in several cyberspace applications.

4.2 Emerging Cyberspace Applications of Wi-Fi

In recent years, researchers have studied a variety of Wi-Fi “RF cloud” features in several cyberspace applications and for the enhancement of local area positioning systems. The idea of using the preamble of OFDM signals, first introduced in HIPERLAN-2/IEEE 802.11a standard, for TOA localization was discussed at the emergence of this standard in [ 59 ]. In this work, the pseudo noise (PN) sequence used in the preamble of the OFDM signal is used for TOA positioning. Like the measurement of timing in GPS, the TOA is measured from the time displacements in the sharp peak of the autocorrelation function of the PN-sequence. It is also possible to measure the TOA from the phase of the received signal; however, this is very sensitive to multipath fading [ 57 ]. OFDM/MIMO systems reduce the multipath allowing a more accurate measurement of TOA using the phase of the received signal. Recently, the measurement of TOA using the phase of Wi-Fi signals with OFDM/MIMO was used for fine-grained micro-robot tracking in [ 68 , 73 ]. The experience in that work suggests that in a line-of-sight (LOS) situation (close distance between transmitter and receiver), where the multipath features are not significant, the phase of the received signal provides a reasonable estimate of the distance. Others have experimented with CSI fingerprints to enhance indoor Wi-Fi positioning [ 74 ]. As we explained in Sect.  4.1 , CSI provides multiple streams of magnitude and phase and the phase information can be used for TOA estimations. The research trend in [ 59 , 68 , 73 , 74 ] opens a horizon for higher precision Wi-Fi positioning, as they are compared to RSS based Wi-Fi positioning in local indoor [ 58 , 75 ], and wider metropolitan areas [ 62 , 76 , 77 ].

In the past decade, the design of novel “cyberspace intelligence” applications that opportunistically benefit from the “RF cloud” radiated from signals used for wireless communications and short range radars, has been a fertile area of research [ 69 ]. These applications take advantage of statistical variations of RF signals propagated from the wireless devices, caused by motion in the environment to design applications that can detect gestures and motion or those used for authentication and security. Because of the widespread deployment and reach of Wi-Fi access points and the availability of Wi-Fi chip sets in almost every personal electronic device, as described in Sects.  2.4 and 2.5, a large body of this literature has evolved around this Wi-Fi RF cloud. Once again, the simplest feature of the Wi-Fi RF cloud is the RSS. Motions in the environment cause multipath fading resulting in changes in the RSS and statistics of this fading behavior as it relates to the speed of motion. The applications benefit from this behavioral change in the RSS to develop simple possibilities in detecting motion related human activity. This trend of research began in the early 2010’s and has evolved throughout that decade. As an example, the time- and frequency-domain multipath fading characteristics of the RSS from body mounted health monitoring sensors was examined in the lead author’s laboratory in the early 2010’s to differentiate among standing, walking, and jogging activities by humans [ 78 ]. In the mid-2010’s, when the infusion of AI to applications became popular, the same idea with more complex activity classifications with AI algorithms was pursued [ 79 ]. In the middle of these activities, hand motion classification using RSS and the frequency offset of OFDM signals from Wi-Fi devices when motion occurs between two devices without any body mounted device was reported to differentiate nine hand gestures [ 80 ]. Research in that direction encouraged the consideration of a more advanced feature such as the CSI for similar applications and suddenly a large body of literature emerged for a variety of related applications for human computer interaction that made extensive use of the statistical behavior of CSI from Wi-Fi signals. Approximately 150 of these papers are classified in [ 81 ]. These papers use CSI toward what is called as “device-free” human activity detection [ 82 ] all the way up to micro-gesture detection applications such as detecting hand motion while typing [ 83 , 84 ].

In gesture and motion detection using RF signals, the work takes advantage of the effects of motion on changing the multipath propagation of signals and the resulting change in statistical behavior of the features of RF cloud to classify human activity. Similarly, it is possible to use the uniqueness of these variations for individual human motions to identify a person. For example, when we train a computer to detect the keystroke of a person using the CSI from Wi-Fi signals, the same system can identify that person as the keystrokes of one individual vary from that of another. This way, using the CSI for keystroke detection can also be used for human authentication for security purposes [ 83 ]. In recent years, a body of literature has also evolved for applications of the Wi-Fi RF cloud in authentication and security. Again, the simplest feature that is used is the RSS and it is possible to use the RSS behavior of body mounted sensors to identify a person [ 85 ]. Fundamentally, authentication is a binary decision-making process (is it Alice or not?) and activity classification is a multiple classification problem (is Alice jogging, walking, standing, or sitting?). In authentication we compare RF feature characteristics of one person to others, while in activity classification we usually compare different activities of a single person. Such similarities have led to the emergence of literature in using more complex CSI from Wi-Fi signals for device-free authentication such as those in [ 86 ]. Another survey of these categories of applications is available in [ 87 ].

The problem of entity authentication is for security – whether an authorized individual is performing an action. However, there is another branch of security application, concerned with generation of unique (and random so that it cannot be guessed) keys for encryption of the data communication between wireless devices sharing this natural broadcasting medium. The fundamental idea comes from the fact that the wireless communication channel between two devices is reciprocal [ 88 , 89 ]. Therefore, when we measure the features of the communication channel between two devices, these features should be the same. However, the details of the electronic implementation of a device is unique to itself and that results in measurements which are not identical. If we can model these differences by a measurement noise, then we can quantize the measured feature based on the measurement noise to establish the same key at two ends of a wireless communication link. A survey of these physical layer security systems is available in [ 90 , 91 ]. Geo-fencing, to ensure that Wi-Fi signal propagation can be confined to the inside of a building is another interesting application of radio propagation for information security [ 92 ].

Although in past decade these cyberspace applications of Wi-Fi signals have attracted significant intellectual attention for research, the commercial market is still waiting for a “Killer App” like Wi-Fi positioning and tracking. The industry is waiting for the next popular application of Wi-Fi signals to enhance cyberspace intelligence further.

5 Conclusions

In this paper we presented a historical perspective of the evolution of Wi-Fi technology in the way that the principal author experienced it (and subsequently the second author) since the inception of this industry in early 1980’s. The paper was prepared as a part of a special issue on the 25 anniversaries of the International Journal of Wireless Information Networks, which was established in 1994 as the first journal fully devoted to wireless networks. In the paper, we began by describing how Wi-Fi has impacted our daily lives and why it is playing this important role. Then we discussed how the dominant physical layer wireless communication technologies, wireless optical, spread spectrum, OFDM and MIMO, and mmWave UWB technologies, were first implemented in the IEEE 802.11 standards for Wi-Fi and how indoor radio propagation studies were conducted to enable these technologies. The rest of the paper illustrated how the RF cloud propagated from Wi-Fi devices enabled important cyberspace applications. We began this part by describing how Wi-Fi positioning revolutionized indoor geolocation science and technology. Then we explained how the RF cloud of Wi-Fi devices has enabled diverse cyberspace applications such as motion and gesture detection as well as authentication and security to hopefully lead the way to another revolution in human computer interfacing.

A version of this paper was originally presented as a keynote speech entitled “Evolution of Wi-Fi Access and Localization – A Historical Perspective”, IEEE VTC, Boston, MA, May 6, 2015. Material presented further evolved in other keynote speeches, the last one in Cybercon’19, Beijing, China, December 16, 2019.

In the recent pandemic, parents and children are using Wi-Fi for work and school, and its untethered feature has made a big difference to the way people have coped with social distancing and quarantines.

We use the term UWB differently here than before, where extremely narrow pulses of bandwidth on the order of a GHz are used for fine grained localization. The term UWB is also now used by commercial 5G systems differently.

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Pahlavan, K., Krishnamurthy, P. Evolution and Impact of Wi-Fi Technology and Applications: A Historical Perspective. Int J Wireless Inf Networks 28 , 3–19 (2021). https://doi.org/10.1007/s10776-020-00501-8

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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.

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

The authors declare no conflict of interest.

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