ISSN: 1389-1286 Vol 194 -20 July 2021

Toward Network: Requirements, Key Technologies, and Challenges in Future Research

a,b,× a Ahmed Shamil Mustafa , Adib Habbal aDepartment of Computer Engineering, Karabuk University, Turkey bAl-Maarif University College,Iraq

A R T I C L E I N F O A B S T R A C T

Keywords: The network technology has recently become widely available, and there are numerous 5G 6G operators in many countries. It’s past time for academics and businesses to focus on the 6G mobile 5G network. At this point, providing a comprehensive overview of the current state as well as a conceptual AI vision of potential developments in the next generation of communications, will be critical. The focus Blockchain of research is shifting to potential wireless communication generations, such as those above 5G or Vision 6G. The 6G is the result of technological advancements in information technology, artificial Key Technologies intelligence (AI), blockchain, and virtual reality (VR). Based on the most recent 5G technology, Taxonomy holographic networking will almost certainly outperform universal communications. Future wireless networking occurs over long distances and across large areas of the globe. Terahertz communications, artificial intelligence, optical networking, blockchain, dynamic holography, big data, free-space optical networking and real-time hyperspectral slicing all support and contribute to the six-generation architecture. In this paper, we look at various core supporting innovations, usage cases, such as technology advancements, new machine learning strategies, connectivity schemes, and networks, to develop a taxonomy. Furthermore, we highlight and solve research problems such as intelligent transversal networks that dynamically evolve, decentralized and stable building designs, and the most prominent academic topics.

1. Introduction system, which runs on a combination of orthogonal

data access with speed of up to few megabits per second [3]. In an effort to accommodate the growing network re- Back in December 2009, Scandinavian capitals such as Oslo quirements, the wireless communication system was built and Stockholm saw the launching of the commercial Long and regenerated every decade [1]. The origin of mobile Term Evolution (LTE) networks, which provided the globe’s telecommunication industry can be traced to the first gener- pioneer 4th generation (4G) service [4]. ation of analog cellular systems (), which was pioneered The proliferation of smart phones was promoted by the

by Nordic Mobile Telephone in Europe and Advanced Mo- ×Corresponding author bile Phone System in the United States of America. These [email protected] (A.S. Mustafa); corporate entities have begun offering mobile voice-calling [email protected] (A. Habbal) services since 1980. Following that, novel generations of ORCID(s): mobile communications have been introduced into the mar- ket every ten years. During the 1990s, the 1G analog systems were substituted by the 2nd generation of digital cellular net- works. Notwithstanding few competing systems, the Global System for Mobile Communications (GSM) successfully enable more than one billion consumers all around the world to utilize mobile voice calls, low-rate data services and short texting [2]. The 3rd generation () system as represented by TD- SCDMA, CDMA2000 and WCDMA were built via a tech- nology termed Code-Division Multiple Access (CDMA) and was introduced in 2001 as a foundation for high-speed

ISSN: 1389-1286 Vol 194 -20 July 2021 frequency-division multiplexing (OFDM) and multi-input from conventional mobile broadband to Internet of Things multi-output (MIMO), thus catering to the mobile Internet (IoT), automated driving, virtual reality (VR) and Industry field. 4.0 [5]. In the past decades, some of the driving forces for Now, we have entered the 5G era with the introduction the growth of wireless technologies include rising demand of 5th generation (5G) communication services. The for high data rate services and impressive growth of notion of 5G has been gathering attention for the past 2 connected devices. years, even to the point of being the source of geopolitical The International Telecommunication Union forecasted tension. While past generations concentrated on enhancing that the mobile data’s volume will rise exponentially, up to the ca- pacity of network, the 5G further expands the role an impressive height of 5 zettabytes per month by 2030 [6]. of mobile communication services from human beings to Nevertheless, there are concerns that the 5G would not be things, as well as from consumers to vertical industries. able to accommodate the rise of applications associated with The mobile subscription’s potential scale has been Internet of Everything, such as VR, mixed reality (MR) and significantly widened from the population of the world to AR, all of which needs a convergence of sensing, computing almost limitless inter- connectivity between humans, things functionalities, control and communication [7]. and machines. It would spur a wide range of service ranging

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[18]. A thorough 6G vision was recently shared by Bariah et 1.1. Limitation of 5G al., who identified seven disruptive technology, challenges, 5G is riddled with challenges in many areas of its func- open research issues and associated requirements [19]. The tion. Despite its ability to support ultrareliable, low-latency THz communication links’ budget was analyzed from the communications (URLLC), the 5G cellular system faces perspective of radio frequency (RF) antenna and hardware, difficulty of being short packet, sensing-based URLLC func- with justified projections of calculus terms including trans- tions that cap the delivery of low-latency and high-reliability mit power, antenna gain and required or achievable noise services with high data rates. This includes mixed reality figure [20]. (MR), virtual reality (VR) and augmented reality (AR). The Furthermore, it evaluate the distance for communica- popularity of applications involving Internet of Everything tions links that are carried out with distinct complexity and will see an integration of sensing, computing, communica- technologies at 300 GHz, directed towards projected 6G tion and control functionalities that have been overlooked in usage. Polese et. highlighted the significant challenges in 5G. Fog computing, edge computing and cloud com- puting introducing the terahertz spectrum usage in mobile networks are among the computing technologies that are sig- nificant in terms of medium access control, transport layer and for distributed computing and processing, network network, while considering the end-to-end (E2E) data flow’s resilience, as well as time synchronization and lower latency. performance on terahertz connections [21]. A survey was 6G should thus be geared towards a vision of being human- carried out on wireless 6G channel models and measure- centric, instead of being application, data, or machine- cen- ments by Wang et al. [22] for full frequency bands that cover tric in order to address 5G’s limitations such as short-packet, optical wireless communication (OWC) channels, mmWave, provide low-latency and high-reliability services with high THz, full coverage such as underwater, satellite and mar- data rates, Internet of Everything (IoE) and system coverage itime acoustic communication channels, and full application [8], and to address the needs of mobile communications in scenarios (such as industry IoT communication channels, the year 2030 and beyond [9, 10, 11]. vehicle-to-vehicle and high-speed train. The potential of integration of VLC in 6G is presented in [23]. The same 1.2. Contribution and Related Works research also explored its technological advances such as As focus increase on the connectivity of 6G, some aca- modulation, ML-based signal processing, underwater trans- demicians around the world have started developing design mission, devices and materials. concepts and hypotheses on 6G network [12, 13, 14]. In The advantages of unamend aerial vehicle (UAV) was general, the discussion of 6G is focused on explaining the discussed by the authors in [24] in order to improve the 6G’s importance of 6G technology for the future. Recently, some capacity and coverage, as well as to suggest a network setup software developers have explored new functions and the that uses tethered UAVs. The idea of 6G trustworthy utility of 6G. Nevertheless, there is an absence of any com- autonomy was proposed by the authors in [25]. The same prehensive investigation on 6G, which considers the inte- study also suggested applicable key performance indicators gration, AI approaches, real-world scenarios and principles to measure trust, and explained about the role of AI in gener- of a network. A majority of survey papers which explored ating quantitative and qualitative modalities of trust. Some 6G have concentrated on one or more of the following excellent methods were proposed by Du et al. in utilizing key aspects: use cases, recent development, taxonomy and ML and AI instruments to enhance networks of 6G in [26]. enabling technologies. This includes energy management, , A thorough review of 6G in terms of requirements, security, allocation of resource and THz communications. direction of research, applications and challenges is pro- Lately, several researches have concentrated on the study vided by a survey paper [15]. There was an introduction of of significant issues linked to the usage of ML within wire- several key technologies including terahertz, wireless opti- less networks [27, 28]. Apparently, novel generations of cal communications, three-dimensional networking, AI and wireless network are able to influence the intelligent func- blockchain. Several authors attempted to raise the stakes for tions via association with ML at edge infrastructure and 5G in future and speculated on visionary technologies which wireless core [29]. The vision and investigation on 6G in the would aid the process of achieving 6G [16]. The vision of de- following decade were highlighted by researchers in [30], velopments in society towards the year 2030 and important who suggested that the 6G’s reality lies in AI and ML. Both requirements of performance for new services and technolo- of these require further research within layers of wireless gies were identified by Liu et al. With consideration of the communication model. This is inclusive of data mining at integration of communication and information technologies, the network, signal processing in the physical layer, etc. a logical mobile network architecture was suggested to in- In addition, 6G’s vision was defined as a complex network corporate 5G network design [17]. A brief demonstration on described by , user side and network edge [12]. several 6G issues was shared by Gui et al., which included re- It was further suggested that emerging paradigms of ML quirements, architectures, challenges, research destinations, with networks of communication may be regarded as core use cases, core services enabling technologies and typical 6G enablers. usage scenarios. An outline of holographic MIMO surface As seen in Table 1, this research tries to propose the most communication which serve as a technological enabler for updated developments for wireless networks and to highlight 6G wireless communication was shared by the authors of

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Table 1 Our survey compared with other survey.

Research Vision Uses Case Key Technologies Machine Learning Scheme Communication and Network Saad, et al. [8] ✓ ✓ ✓ ✓ Zong, et al. [9] ✓ ✓ Yang, et al. [14] ✓ ✓ Chowdhury, et al. [15] ✓ ✓ Liu, et al. [17] ✓ Bariah, et al. [19] ✓ ✓ ✓ Akyildiz, et al. [31] ✓ ✓ ✓ Sheth, et al. [32] ✓ ✓ ✓ Jiang, et al. [33] ✓ ✓ Our Survey ✓ ✓ ✓ ✓ ✓

the most advanced enhancements in our investigation. There New paradigm shifts will be introduced via 6G wireless is also room for creativity and discovery. The major goal of communication networks. Table 2 illustrates the vision of this research is to explore and set up a discussion of modern 6G network as suggested by [35]. Firstly, in order to provide advanced in 6G systems. Next, a taxonomy of 6G wireless a wholesome and global coverage, the 6G wireless commu- systems was generated following use cases, communication nication networks shall be space-air-sea-ground integrated technologies, computing technologies, key enablers, emerg- networks. The range of wireless communication networks’ ing machine learning schemes and networking technologies. coverage will be extended by UAV communication, mar- The final contribution is discussing few issues affecting open itime communication, satellite communication. All spectra research and plausible solutions for future investigations. will be completely explored in order to provide a higher data rate, which shall include mmWave, optical frequency bands, 1.3. Rest of paper sub-6 GHz and THz. The ML and AI technologies shall be The following is the survey’s structure in figure 1. Sec- effectively integrated with 6G wireless communication net- tion 2 provides a 6G Vision and Services Requirements works to allow full applications. This would lead to greater highlight on how the new generation will provide a higher network automation and management. IN addition, the dy- data rate, enable full applications, different types of Key namic orchestration of caching, computing and networking Performance Indicator (KPI) associated services, and a com- would be enabled by AI technology, thus enhancing the next- parative study between 5G and 6G. In Section 3, we present a generation networks’ performance. Lastly, another trend is 6G taxonomy that includes enabling technologies, emerging the endogenous and strong network security for both network machine learning, and network communication technolo- and physical layer during its development. The development gies. In addition, this section, will present and discuss novel of 6G wireless communication networks will be boosted technologies which will be critical in the core of the 6G by the industry verticals, such as Internet of Things (IoT) network. Section 4 presents the challenges of many of the industry automation, federated learning systems, digital twin technologies discussed in the previous section, followed by body area network, cellular vehicle to everything (C-V2X), a future direction in many major technologies. Finally, the energy efficient wireless network control and cloud VR. conclusion of this work. 2.1. Service of 6G 2. 6G Vision and Requirements Conversely, there are expectations that the 6G communi- cation system’s service requirements are to be featured by the The increasingly advanced 5G has promoted the idea of following key performance indicators linked services [36]: an even more advanced wireless system, such as the sixth generation wireless system, which should host an abundant • Ultra-high-speed with low-latency communications autonomous service that consists of the past and future (uHSLLC) trends. Specifically, the 6G is said to present new wireless • Ubiquitous mobile ultra-broadband (uMUB) technologies as well as innovative network architecture. It has been postulated that 6G will eventually lead to the next- • Ultra-high data density (uHDD) generation of connectivity, which is propelled by the evolution from ‘everything connected’ to ‘connected intelli- • Massive machine-type communication (mMTC) gence’, thus allowing the interconnectivity of Human-Thing- Intelligence. Moreover, it will be able to host highly precise Different 6G service type features are presented in emerging technologies. 6G technologies explained under communication for haptic and tactile applications in order to create multi-level sensory experience such as touch, hearing, uHSLLC, uHDD, uMUB and mMTc services are shown smell and vision [33, 34]. in figure 2. One or more services may be boosted by each method.

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Figure 1: Structure of paper

As seen in Table 3, a study that compares 6G and 5G [37]. 6G is estimated to permit Gbps coverage anywhere is assessed from many aspects such as the capability of the with the inclusive coverage of environments such as the sea system and the necessary specifications. (20 nautical miles) and the sky (10,000 km) [37]. 6G would Projections suggest the 6G system possesses thousand also adopt the volume , compared to the times greater simultaneous wireless connectivity compared popular area spectral efficiency [16]. Ultra-long battery life to 5G system [15]. In comparison to enhanced mobile broad- and advanced battery technology for harvesting of energy band (eMBB) in 5G, the 6G would have ubiquitous services will also be included in the 6G system. Within the system of such as uMUB. One of the key features of 5G which is ultra- 6G, there is no need to separately charge the mobile devices. reliable low-latency communications, would also play an important role in 6G communications permitting uHSLLC 2.2. Uses Case by integrating features including greater than 99.9999 % 6G wireless system spreads use cases of 5G in the reliability [37], 1Tbps rate of peak data and E2E delay of extreme end. 6G adds to the issues that are noted in 5G lower than 1 ms [16]. The 6G communication system would for instance limitations in delivering highly reliable services include massively linked devices (up to 10 million/km2) and short-packet. Furthermore, the cost of RAN hardware

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Figure 2: Service of 5G and 6G [9]

Table 2 Table 3 6G vision. Compares between 5G and 6G

Vision Main Sub

5G 6G Global coverage Terrestrial

Maritime Year Early of 2020 2030 expected UAV The spectrum 3 - 300 GHz 1-10 THz Satellite Bandwidth 250 MHz – 1 GHz 3 THz and more Data rate 1 - 20 Gbps More than 1 Tbps Every possible spectrum mmWave Efficiency of spectral 30 bps / Hz 100 bps / Hz THz The mobility + 500 km/h + 1000 km/h Vision of 6G Optical E2E delay 5 ms < 1 ms Sub – 6 GHz E2E reliability 99.999 % 99.99999 % Radio delay 100ns 10ns Full Applications Big data Service type uRLLC, eMBB uHDD, uHSLLC, AI and mMTC uMUB, and mMTc

High level of security Network layer Physical layer

of connectivity [40]. 6G Extended URLLC and eMBB can ensure health care accessibility for remote places. makes sustainability a challenging issue [38]. Nevertheless, 6G technologies with its increased bandwidth and 6G technologies take into consideration the socio-economic intelligent network, will be able to guarantee e-health and environmental issues when integrating IoE services care in places that have never experience it before [41]. technologies. • Unmanned Mobility: The air-ground-underwater com- • Teleportation: Holographic teleportation has been munication requires heterogeneous networking. The accepted as natural successor to AR and VR-based growing number of sensors in autonomous vehi- cle solutions. Compared to the pre-existing solutions, need a few terabits data rate per driving hour [42]. holographic teleportation functions in a true three- The utilization of 6G technology integration in dimensional space. It needs data rates close to 5 Tbps autonomous vehicles faces many challenges in the and an end-to-end latency of less than 1 ms [39]. deployment of hardware, software and RAN. This 6G, with its projected Tbps-level throughput and sub- study examined vehicles which are fully indepen- millisecond latencies, would play a crucial part in dently connected and can provide safe driving, un- building upon the groundwork founded by URLLC manned mobility, smart infotainment, and improved and eMBB. traffic management [41]. • Health Care: The success of remote healthcare solu- • Holographic services: This study is founded on a tions depends mainly on the availability and quality remote connection with an ultrahigh accuracy [41]. Page 5 of 14

a network management’s cloud computing paradigm. Data rate limitations for 3D holographic display with In this case, the network plays a central role to man- no raw compression hologram, full colors, full duplex, age the flow of traffic and to managed the allocation and 30 fps require 4.32 Tbps including latency in the of network resource for optimization of performance order of sub-ms [9]. [52]. • Other: Sustainable high-precision industry 4.0 are • Mobile Edge Computing: The mobility-enhanced edge examined in this survey paper [43]. Massive URLLC, computing (MEEC) is important to 6G technolo- gies Haptics communication, Human-centric services, Nano- as a result of the distributed massive cloud Internet of Things and Bio-Internet of Things are applications. The AI-based MEEC would leverage the studied by [44]. computation on system control and big data an- alytics to the edge. Edge intelligence serves as a new 3. Key Technologies paradigm that fulfills the needs of upcoming ubiquitous service settings of heterogeneous com- 6G is essentially developed as an upgrade from 5G, putation, high-dimensional intelligent configurations meaning there would be fine-tune of existing functions and and communication. The emerging technologies pre- introduction of new ones. A wide range of innovations may sented above may be characterized by distinct types influence the architecture of 6G. of 6G service [15]. The edge computing is arguably a better technology to meet this target. Recently, there 3.1. Enabled Technologies is general shift in explorations of computational In this section, we will present and discuss a variety of operations from centralized cloud, with distant data technologies that are proposed for use in a 6G network. centers, to more decentralized computing that lies • Blockchain: Another important aspect of 6G net- nearer to (or edge of) the source of the network. work’s technology is blockchain. It includes dis- Owing to the limited capability in edge computing tributed ledger technology, network decentralization servers, AI algorithms that are more lightweight may and spectrum sharing. Lately, there are researchers be used to provide smart applications for edge settings. that have investigated the potential of blockchain in RL-based edge computing resource management is 5G [45]. Its application enhances edge computing, essentially a model free scheme that will not require device-to-device communications, implementation of any historical knowledge. It can also be utilized to significant services, NFV and network slicing. Local study the dynamics of the environment and propose computing power for mobile blockchain systems may real-time control decisions [53]. be aided by the ubiquitous connectivity, storage re- • Homomorphic Encryption: Homomorphic encryption sources and computing which are provided by mobile (HE) permits computations on data that are encrypted, networks [46, 47]. This would support the solving which includes constant-multiplication (cmul), addi- of PoW puzzles, consensus execution, encryption, tion (add) and multiplication (mul). When they are de- and hashing. It is foreseen that blockchain can be crypted, they would match the outcome of operations integrated into the forthcoming 6G system for better that were carried out over plaintext. Such operation is security, information infrastructure and flexibility. able to shield any queries from the cloud servers. Pri- Network decentralization is also utilized by S. Dang vacy protection between data users and cloud servers et al. to enhance the performance of the network [48]. would also be enhanced [54]. Authentication security is also improved by Strinati et al. via distributed ledger technology [49]. The low • Communication and Sensing: Autonomous wireless use of spectrum and spectrum monopoly may also networks rely on the ability to sense dynamically be addressed by blockchain technology [50]. The altering conditions of the environment in a continuous blockchain privacy relates to access control, com- manner. Then, it would be necessary to share such munication and authentication. The authentication data among the distinct nodes [55]. Within 6G, such and secured network access functions via blockchain sensing will be closely linked to communication in technology are highlighted by Ling et al. [51]. order to aid autonomous systems. Real challenging factors in order to achieve such integration include • Network Slicing: Network splicing enables a network complex nature of communication resources, multi- operator to permit virtual networks that are dedicated level cache resources, larger amount of sensing objects to aid the delivery of any service for users includ- and multilevel computing resources [56, 57]. ing industries, machines and vehicles. It plays a cru- cial management function in systems whereby a large • Energy Transfer and harvesting: The wireless sys- number of users are linked many heterogenous net- tem’s sixth generation focuses on a sustainable society works in the 5GB communication systems. Network that is driven by data, secure, unlimited, sustainable function virtualization and software-defined network- and near-instant [58]. A truly borderless society will ing are the crucial enabling methods for the imple- be achieved via ubiquitous user-experienced-based mentation of dynamic network slicing. These affects

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Furthermore, the industry and research fields have hyperdata connectivity and breaking down of regional growing interest in energy harvesting (EH) methods barriers [8]. The 6G’s multilevel communication abili- for external recharge of batteries or to minimize re- ties will have a significant contribution towards global placement [69], which is a costly and at times im- sustainability and would greatly support distinct ser- possible in building structures, human body or en- vices at the layer of application [59]. The digital vironments that are hazardous. As such, EH has a infrastructure will be provided by 6G to meet the growing stake in the potential of IoT networks since it requirements of society. Online monitoring and sup- allows i) improved energy efficiency and networkwide port of the growth of the SDGs ecosystem would be minimization of emission footprint, and ii) wireless aided by 6G’s context-aware environment sensing and charging, which eases the maintenance of IoT devices indoor localization. [59] presents an outline of the and raises their durability (owing to the contact free KPIs for all the 17 SDGs, which include probable feature). EH is a sustainable solution to processing of use cases. The final goal of energy efficiency is to waste and battery recharging issues [70]. achieve a green society- one that is aided by net- works of 6G via zero-emission/energy/cost Internet • Intelligent Radio (IR): Upcoming hardware revolu- of Things (IoT) deployment [60]. Nevertheless, the tions especially those in circuit systems and RF, would insufficient number of mature solutions to power and promote the growth of 6G to fully utilize the enhance- sustain uninterrupted operations of the larger number ments of base-station level and device-level hard- of devices presents a significant concern. Sustainabil- ware. We foresee that an algorithm-hardware sepa- ity will be greatly improved by technological advances ration architecture would be crucial in 6G. Specif- in the fields of machine learning (ML), artificial intel- ically, a transceiver algorithm would have the abil- ity ligence (AI), visible light communications, molecular, to project the transceiver hardware’s ability over fog/edge computing, metamaterials/met surfaces and which the protocol runs. Then it would permit self- backscatter will definitely aid sustainability [31, 61]. configuration following the capability of the hard- ware. This is in stark contrast to the systems of 1G- An interesting matter for future wireless networks 5G in which the transceiver algorithms and devices is energy efficiency. Hardware that is compatible with are designed in a joint manner. Traditionally, hardware 6G’s energy requirements is immensely needed. capabilities such as RF chains, ADCs’ sampling rate, Wireless charging was explored based on Ambient amount of antennas and resolution have remained Backscatter Communication System (ABCS), which quasi-static. Nonetheless, latest state-of-the-art anten- is an Energy Harvesting (EH) method. This would nas and circuits’ advances are ramping up enhance- permit battery life that is longer [62], as it provides ments of the hardware capabilities. devices with an alternative source of power from wireless communication. Energy requirement chal- This makes it possible for the diversification and up- lenges at the mobile unit would be resolved by the Si- grade of the 6G base station (BS) and handset. Simply multaneous Wireless and Information Power Transfer put, the 6G would not be able to function under tradi- (SWIPT) [63], should it be provided via 6G [64, 65]. tional joint designs, which do not permit agile adapta- Our hypothesis is that this would permit the Internet tion to an upgradable and diversified hardware. In or- of Bio-ano Things, haptics and other functions that der to address weaknesses of joint hardware-algorithm possess extremely strict requirements of energy. design and maximize the advantages of the algorithm- hardware separation architecture, we hereby propose In terms future sustainable methods of energizing the an operating system between transceiver algorithms growing number of linked devices, energy harvest- and device hardware, in which we can consider a ings seem to be the focus area of investigation. Its aim transceiver algorithm as a software running over the is to substitute traditional methods of powering operating system. In addition to its ability to pre- sensors and devices by utilizing energy from ambient dict the abilities of phase shifters, antennas, local RF environment. Man-made and natural sources are the chains, ADCs and etc., it would also be able to auto- two groups of resources for harvesting of energy [66]. matically measure their analog parameters. Following Examples of natural sources or renewable resources hardware data and AI techniques, the operating system include mechanical vibrations, microbial fuel cell, will be able to configure its own transceiver algo- human activity powered, solar, thermal and wind [67]. rithms through an interface language. This framework Man-made energy harvesting takes place via Wireless shall hereby be referred to as intelligent radio (IR). Energy Transfer (WET) in which dedicated power beacon is utilized in the transfer of energy from source IR is essentially a broader concept that relies on the to destination [68]. The unpredictable and periodic na- algorithm-hardware separation architecture, com- ture of harvesting natural energy resources reduces the pared to the learning based intelligent PHY layer that guarantee of QoS. As such, there is growing research would be explored in later sections. Firstly, the IR into WET [67]. will train its DNNS’ receiver and transmitter side via transfer of labeled training data. Then it would convey

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data symbols may be used to direct the model by bits of data upon meeting its targeted requirement grouping together points for developing nonlin- of performance. Since AI-chips have recently experi- ear decision boundaries [77]. This approach is enced significant enhancements, the IR would enable usually utilized to group systems via detection a flexible and inexpensive solution for 6G. The AI of useful clusters within input data [78]. K- chips would aid DNN-based IR and create a paradigm- Means Clustering, maximum likelihood learn- shift hardware architecture of 6G transceivers since ing and Principal Component Analysis (PCA) they are able to run DNNs at low state of power. IT are some of the learning approaches utilized for also considers protocols over layer 3, which are unsupervised learning [12]. It has the possibility upgradable in nature to aid distinct applications of AI. to carry out a variety of operational features By utilizing IR, the 6G would be able to assess the linked with distribution estimation, distribution- role of distinct hardware components and aids specific samples generation, features classifica- identification of hardware bottlenecks, which in turn tion and features extraction. drives device manufacturer to optimize the allocation 3. Reinforcement learning: It attempts to raise the of budget towards costs of hardware. In return, the IR reward, i.e., it adopts best-suited decisions on would reduce the running time of 6G and contribute which path of actions should be taken via to a drastic decrease in the cost of novel hardware and interaction with surrounding environment. This algorithms, thus increasing the rate of its own means it receives feedback from the system and evolution [71]. generates better outcomes. [79] suggests a • Schemes of Machine Learning: Machine learning ap- model-free distributed reinforcement learning proaches such as unsupervised learning, reinforced approach for provision of power, in which QoS learning and supervise learning are widely utilized in indicators and Channel State Information (CSI) addressing recent issues and improving communica- are utilized in the transmission of power. Classic tion system’s efficiency via 6G networks. At present, reinforcement learning algorithms include Q- DL and ML are used to assist mobile networks in learning, actor-actor-critic (AC), Markov Deci- almost all facets of their application. AI is essentially a sion Process (MDP), policy learning and value- multi-disciplinary science which techniques, theories, based reinforcement learning [32]. and methodologies to drive simulation and extension 3.2. Communication and Network Technologies of human intelligence [72]. AI tries to comprehend the • Quantum Communications: In the field of 6G net- basics of intelligence and run a simulation of human works, unsupervised reinforcement learning within brain’s processing of information via machines. ML networks has tremendous potential. For the purpose being a part of AI, is associated with computational of labeling huge volumes of information collected in predictions and statistics by harnessing information 6G, there is a lack of feasibility in supervised learning and experience obtained from data [73, 74]. DL is approaches. Labeling is not necessary in unsupervised a part of ML that allows a model to design deci- sions, learning. As such, this method may be utilized for predictions and classifications following large autonomous generation of representations for com- datasets, without needing any explicit programming. plex networks. It is feasible to run the network in an 1. Supervised learning: In supervised learning, the autonomous manner via integration of unsupervised machine adapts following training information learning and reinforcement learning [12]. Security in which each input is mapped with a specific against distinct cyber-attacks in 6G will be provided target. It would need a predefined information via use of advanced quantum computing and quantum set to learn and raise the system’s performance. communication technologies [48]. Core 6G enablers Basically, there are two groups which are clas- include the emerging fields of quantum machine learn- sification and regression algorithms. The clas- ing, quantum computing, and their synergies with sification algorithm generally labels each in- put communication networks. In order to address complex data, which mostly consists of logistic re- challenges, it is necessary to escalate quantum com- gression, Support Vector Machines (SVM), k- puting engineering and computing. Application of nearest neighbors (KNN) and Decision Trees quantum key following quantum no-cloning theorem (DT) while regression algorithm estimates the and uncertainty principle, good security is offered by continuous or real values following available quantum communications. Quantum or photon parti- input features [75]. cles are utilized to encode the data in the quantum 2. Unsupervised learning: Training data in unsu- state. As a result of quantum principles, accessing and pervised learning is unlabeled. Thus, the values cloning them is impossible without tampering it [61]. are not denoted to any specific class [76]. There In addition, the qubits’ superposition nature enables is an absence of any prior data of the intended enhancement of quantum communication. system in this form of learning. For example, based on the constellation maps, the input noisy

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influenced by electromagnetic radiation such as the • THz Communication: The THz band is the spectral hospital and aircraft [83]. band between optical and microwave bands, with a The lighting technology influences the greatest achiev- frequency range between 10 and 0.1 THz. There are able data rate [84]. The data rate following RGB LED various unique traits that encourage the utility of THz can be up to multi-Gb/s while the data rate for bands for future communications networks, with the VLC following phosphor coated blue LED can reach exception of its abundant spectrum resources that are 1 Gb/s. Micro-LED is the best functioning LED less developed [80]: technology which has data rates exceeding 10 Gb/s 1. Up to 100 Gbps or more data rates are promised within the lab setting [85]. Considering the spurt of by the THz communication systems with tens of growth in the luminous efficiency and life of LED GHz available bands in the spectrum of THz. On lamps, together with the improvements of associated the other hand, the mm-Wave band only consists technologies such as digital modulation technology, of 9 GHz bandwidth [78]. there are expectations that the VLC would attain data 2. Secure communication is possible via THz wave rates of hundreds of Gb/s or even Tb/s upon reaching as a result of the short pulse duration and narrow the era of 6G. beam that significantly limits the probability of • 3D Networking: The airborne and ground networks any eavesdropping. will be integrated by the 6G system to aid user com- 3. With a small attenuation, the THz waves have munication in the vertical extension. The UAVs and the ability to penetrate some materials, which low orbit satellites provide the 3D BSs [86, 87]. The is rightly fitting in certain scenarios. With such 3D connectivity is significantly distinct from tradi- traits, there are various potentials of THz wave’s tional 2D networks considering the integration of application in ultra-high speed space communi- novel dimensions of altitude and related degrees of cation and wireless communication [61]. freedom. 3D coverage is provided by the 6G het- • Visible Light Communications: A complementary erogenous networks. Stringent seamless access and technology for mobile communications that are based global coverage including for mountain terrains and on RF is Optical Wireless Communications (OWC). oceans will be available following the decentralized The frequency range consists of visible light, ultravi- 6G networks with the integration of UAV networks, olet and infrared spectrum. As a result of the heavy terrestrial networks and satellite systems. usage of LED and technological advancements, the • Bio and Nano networking: Nano, optical, and bio- OWC’s spectrum with the greatest potential is the networking are the alternative networking architec- visible light spectrum (430-790THz). One of LED’s tures of 6G. The basis for the actions of N-IoT is major difference from other illumination technology molecular correspondence. B-IoT is utilized based is its capability to immediately alter its intensity of on biological cells for communicate using IoT. De- light. This enables encoding of data in emitted light spite facing certain challenges, the N-IoT and B-IoT in various methods [81]. In order to meet the two aims are integral of potential 6G intelligent networks. The of high-speed data communication and lightning, visible molecular contact physical layer technology’s archi- light communication (VLC) takes full advan- tage of tecture is a very challenging area. With the exception LED. A comparison between traditional radio of physical layer approaches, new routing approaches communication and VLC for short range links up to must be suggested in order to reach a significantly several meters possess greater benefits [82]. First and distinct N-IoT and B-IoT when compared to standard foremost, free and unlicensed ultra-high bandwidth IoT. There must be creation of efficient biodevices and (THz) is provided by visible light spectrum. Next, nanodevices [88]. opaque obstructions are impenetrable by visible light, VLC’s transmission medium. This implies that the network information’s transmission is linked to a sin- 4. Future Research Direction gle building. It also means that receivers located at the Several technical challenges that disrupt the advance- exterior of the building are not able to receive signals. ment of 6G network includes peak throughput, flexibility of This assures that a high level of data transmission connection, terahertz waves, greater energy efficiency and security. It also minimizes inter-cell interference that self-aggregating communication fabrics; the non-technical is noted as a serious matter in RF communication of issues such as usage rules, policies, industry barriers, regu- high-frequency. Thirdly, illumination sources are used lation and spectrum allocation. by VLC as base stations. This does not require costly constructions of base station. RF communication also 4.1. Terahertz waves does not require any maintenance cost. Lastly, VLC is Although, the terahertz frequency band provides has a not affected by external electromagnetic interference lot of benefits in the field of mobile communications, it also and does not build up any electromagnetic radiation. has to face plenty of challenges [18, 89, 90]: In terms of Thus, it is suited for unique scenarios which are easily

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the number of transmissions by implementing a wake and coverage, the terahertz propagation qualities and the large sleep schedule at the base stations. For example Wang et number of antennas involved shows that terahertz commu- al. [93] have suggested a hybrid energy powered cellular nication is mainly directional beam signal propagation. The network which can aid in planning the strategies for wake- mechanism involved in this highly directional propagation up at base stations so that can increase energy efficiency and signal function need to be redesigned and optimized. An- balance the network resources. Similarly the same strategies other feature is large-scale fading whereby the terahertz can be utilized for selecting routes, network load balancing signal is extremely sensitive to shadows which can greatly and switching. effect coverage. Nevertheless, rainfall/humidity has fading effect on THz. 4.3. AI Empowered Network A few terahertz frequency bands with relatively small It is essential to deeply embed AI solutions in the future rain attenuation can be chosen as typical frequency bands for G networks in order to allow hyper-intelligent networking future terahertz communications for example the frequency mainly when the solution cannot be acquired efficiently or bands around 140 GHz, 220 GHz, and 340 GHz. As for in close forms using traditional methods. AI frameworks intermittent connection and Fast channel fluctuation from a including deep learning, deep reinforcement learning [94], system perspective, the connection of the terahertz com- machine learning [12], distributed learning [95], computa- munication system appears to be greatly irregular. Hence, tional intelligence [96] and FL [97] have been studied to a mechanism which is fast adaptive is necessary to solve this allow hyper-intelligent networking for instance AI-driven fast changing irregular connection problem. Power con- intelligent orchestra, air interface and node programing. AI sumption. The huge antennas and large bandwidth in the ter- can help in automatically avoiding conflict in 6G. Since 6G ahertz band requires high-resolution quantization; the usage networks are gearing towards none infrastructure networks of low-cost devices and low-power will pose a big problem. conflicts arising from sharing resources and minimizing The spectrum regulation, ITU requires 0.12 THz to 0.2 THz task delays cannot be avoided. Moreover, operators and for wireless. service providers might be involved in greedy behavior and create conflicts to maximize their profits. These conflicts 4.2. Energy efficiency will influence the provision of services and user experience. The 6G networks will develop ultra-large-scale ubiqui- Hence, it is important to solve all the conflicts that occur tous wireless nodes, ultra-high throughput and ultra-wide while handling different technologies in terms of operators, bandwidth that will present huge energy consumption issues. resources and networks [98]. One method to overcome con- Therefore, it is crucial to lessen the energy consumption flicts is to utilize AI for developing conflict management per bit. Green energy saving communication will perform a policies [38]. First, the policy can detect any conflict that crucial role in overcoming the huge energy consumption de- happens and suggests an action whereby the user needs to mand of the future 6 G network, [50]. Besides, AI techniques prioritize on improving service quality. A module contain- can facilitate devices and infrastructures to optimize energy ing policies for detecting conflicts and resolution is highly management strategies and hereby increasing usage time recommended to be integrated into the 6G communication by carefully controlling their harvesting energy or energy system network for seamless and user friendly services. Even consumption namely wireless power transfer and energy though, AI solutions perform better compared to conven- harvesting. Hence, energy management is a challenging but tional optimization methods, it still has a few limitations crucial issue for 6G networks. Moreover, achieving sus- pertaining to the usage of AI in wireless communication tainability in communication systems is a challenging task networks. As an example, the performance of deep learning and efforts must be made from all angles. Verma et al. methods depends on the size and quality of datasets utilized [91] suggested the usage of hybrid whale spotted hyena for training. Nevertheless, the prerequisites of a standard optimization to enrich the selection of cluster heads in 6G to dataset of highly complex and dynamic networks is usually allow communication. Even though, it is one of the protocols unavailable. Thus, it is difficult to compare the performance which can be utilized but actually there are other communi- of different AI solutions for the same problem. Furthermore, cation protocols and network resource management which in many cases traditional methods still perform well partic- can be optimized by the said. Another problem is to offload ularly when closed-form and/or non-extract but high-quality some of the centralized services to nearby edge computing solutions can be obtained. Hence, holistic usage of both AI facilities that can be attained through federated and transfer and conventional methods is recommended to allow hyper- learning strategies. Liu et al. [92] have recently suggested intelligence in future 6G networks. the usage of FL methods for 6G communications. Neverthe- less, the same can be leveraged for designing personalized 4.4. Data Rate systems, or using cached content for user centric services One of the main technical indicators which is engaged which could decrease the problem from access networks. since the first generation of wireless mobile communication New radios and base stations using 5G and 6G networks can systems is data rate. The peak rate will increase further with also utilize scheduling services. Multiple domains which 6 G and be known as the Terabit era (Tb/s). Nevertheless, the include AI and optimization methods combined with 6G big data general-purpose applications means a large quantity of technology and helped in decreasing the energy required or data transmission will be applicable. Furthermore, the

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and low latency algorithms. Hence, when compared to tra- AR/VR and holographic communication will be supported ditional computer tasks, accuracy and latency trade-off are by 6G applications. Hence, the data rate requirement would more important now. exceed other wireless applications [99]. 4.5. Connection everywhere 5. Conclusion Future 6G communication network should target every- When a new generation of communications technology one can communicate with any related object for obtaining is introduced, it brings with it novel and exciting services. valuable information at anytime and anywhere [1]. The 5G communication system which has some impressive qualities was formally launched globally in 2020. Despite 4.6. Management for 3D Networking the enormous demand for wireless communication, 5G will The 3D networking extends in vertical direction. Thus, be unable to meet it by 2030. As the 6G network enables a new dimension was further added. Furthermore, multi- ple terabit-per-second speed and attracts an average of 1000+ adversaries may interrupt legitimate information and cause wireless devices per person, AI will be critical to complex overall system performance to significantly down- grade. issues on the 6G wireless network. This study introduced Hence, new methods for multiple access, routing protocol, the new capabilities of 6G systems, developed a taxonomy optimization for mobility support and resource management based on multiple parameters, and provided a set of useful are necessary. Scheduling will require a new network guidelines for determining open issues. designing. 4.7. Intelligent network security References AI provides challenges and opportunities to 6G users’ [1] S. Zhang, J. Liu, H. Guo, M. Qi, N. Kato, Envisioning device-to- privacy protection and network security. Majority of the device communications in 6g, IEEE Network 34 (3) (2020) 86–91. previous studies are based on fixed network. 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and conference proceedings in the areas of Fu- ture [97] Q. Yang, Y. Liu, T. Chen, Y. Tong, Federated machine learning: Internet, and performance evaluation. His pro- Concept and applications, ACM Transactions on Intelligent Systems fessional experience includes being a speaker at and Technology (TIST) 10 (2) (2019) 1–19. a number of renowned research conferences and [98] T. Darwish, G. K. Kurt, H. Yanikomeroglu, G. Senarath, P. Zhu, A technical meetings such as IEEE, internet2, APAN, vision of self-evolving network management for future intelligent and APRICOT, an editor for top tier and refereed vertical hetnet, arXiv preprint arXiv:2009.02771 (2020). journals, a technical program committee for IEEE [99] Y. Zhao, G. Yu, H. Xu, 6g mobile communication network: vision, conferences on computing networks as well as an challenges and key technologies, arXiv preprint arXiv:1905.04983 examiner for postgraduate scholars in his research (2019). areas. His research interests include Future Internet [100] H. Fang, X. Wang, S. Tomasin, Machine learning for intelligent protocols and architecture, 5Th Generation Mobile authentication in 5g and beyond wireless networks, IEEE Wireless Networks, as well as Blockchain Technology and Communications 26 (5) (2019) 55–61. Digital Trust. [101] L. Xiao, C. Xie, M. Min, W. Zhuang, User-centric view of unmanned aerial vehicle transmission against smart attacks, IEEE Transactions on Vehicular Technology 67 (4) (2017) 3420–3430. [102] M. Jagielski, A. Oprea, B. Biggio, C. Liu, C. Nita-Rotaru, B. Li, Manipulating machine learning: Poisoning attacks and countermea- sures for regression learning, in: 2018 IEEE Symposium on Security and Privacy (SP), IEEE, pp. 19–35. [103] M. S. Ali, M. Vecchio, M. Pincheira, K. Dolui, F. Antonelli, M. H. Rehmani, Applications of blockchains in the internet of things: A comprehensive survey, IEEE Communications Surveys Tutorials 21 (2) (2018) 1676–1717. [104] J. Xie, H. Tang, T. Huang, F. R. Yu, R. Xie, J. Liu, Y. Liu, A survey of blockchain technology applied to smart cities: Research issues and challenges, IEEE Communications Surveys Tutorials 21 (3) (2019) 2794–2830.

Ahmed Shamil Mustafa was born in Baghdad, Iraq. He received his Master of Communication and Computer Engineering from Universiti Ke- bangsaan Malaysia (UKM), Malaysia in 2015. He is currently pursuing a Ph.D. degree in the depart- ment of computer engineering, Karabuk Univer- sity, Turkey. His research interests include Wireless and Mobile Communications, VANET, AI, and Cryptography.

Adib Habbal is a Professor (Associate) of Com- puter Engineering at Karabuk University, Turkey. Before joining Karabuk University in 2019, he was a senior lecturer at Universiti Utara Malaysia (ten years) and head of InterNetWorks Research Plat- form (three years). He also served as IEEE UUM Student Branch Founding Counselor and Executive Council Member of the Internet Society Malaysia Chapter. Dr. Habbal received his Ph.D. degree in Computer Science (specializing in Networked Computing) from Universiti Utara Malaysia. Dr. Habbal has received a number of recognitions from Universiti Utara Malaysia (UUM) for his outstand- ing educational and research activities including the Excellent Service Award (2010), Best Research Award (2014), Prolific Writer Award (2016) and many others. He has been the recipient of Inter- net Society Fellowship to the Internet Engineer- ing Task Force (IETF), an IEEE Malaysia Section Best Volunteer Award, and an Asia-Pacific Ad- vanced Network (APAN) Fellowship. Dr. Habbal is a senior member of the Institute of Electrical and Electronic Engineers (IEEE). Dr. Habbal’s re- search projects have been funded by several or- ganizations, including IEEE R10, IEEE Malaysia Section, Internet society, Malaysian Ministry of Higher Education, Universiti Utara Malaysia and others. He has over 80 publications in journals

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