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6G Wireless Systems: A Vision, Architectural Elements, and Future Directions

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Abstract

Internet of everything (IoE)-based smart services are expected to gain immense popularity in the future, which raises the need for next-generation wireless networks. Although fifth-generation (5G) networks can support various IoE services, they might not be able to completely fulfill the requirements of novel applications. Sixth-generation (6G) wireless systems are envisioned to overcome 5G network limitations. In this paper, we explore recent advances made toward enabling 6G systems. We devise a taxonomy based on key enabling technologies, use cases, emerging machine learning schemes, communication technologies, networking technologies, and computing technologies. Furthermore, we identify and discuss open research challenges, such as artificial-intelligence-based adaptive transceivers, intelligent wireless energy harvesting, decentralized and secure business models, intelligent cell-less architecture, and distributed security models. We propose practical guidelines including deep Q-learning and federated learning-based transceivers, blockchain-based secure business models, homomorphic encryption, and distributed-ledger-based authentication schemes to cope with these challenges. Finally, we outline and recommend several future directions.
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Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
Digital Object Identifier XXXX /ACCESS.2019.XXX
6G Wireless Systems: A Vision,
Architectural Elements, and Future
Directions
LATIF U. KHAN1, IBRAR YAQOOB1, (Senior Member,
IEEE), MUHAMMAD IMRAN1,2, ZHU HAN1,3 , (Fellow, IEEE), and CHOONG SEON HONG1,
(Senior Member, IEEE)
1Department of Computer Science & Engineering, Kyung Hee University, Yongin-Si 17104, South Korea.
2College of Applied Computer Science, King Saud University, Riyadh 11451, Saudi Arabia.
3Electrical and Computer Engineering Department, University of Houston, Houston TX 77004, USA.
Corresponding author: Choong Seon Hong (e-mail: cshong@khu.ac.kr).
This work was partially supported by Institute of Information communications Technology Planning Evaluation (IITP) grant funded by
the Korea government(MSIT) (No.2019-0-01287, Evolvable Deep Learning Model Generation Platform for Edge Computing) and by the
MSIT(Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program
(IITP-2020-2015-0-00742) supervised by the IITP(Institute for Information communications Technology Planning Evaluation).
ABSTRACT Internet of everything (IoE)-based smart services are expected to gain immense popularity
in the future, which raises the need for next-generation wireless networks. Although fifth-generation (5G)
networks can support various IoE services, they might not be able to completely fulfill the requirements
of novel applications. Sixth-generation (6G) wireless systems are envisioned to overcome 5G network
limitations. In this paper, we explore recent advances made toward enabling 6G systems. We devise a taxon-
omy based on key enabling technologies, use cases, emerging machine learning schemes, communication
technologies, networking technologies, and computing technologies. Furthermore, we identify and discuss
open research challenges, such as artificial-intelligence-based adaptive transceivers, intelligent wireless en-
ergy harvesting, decentralized and secure business models, intelligent cell-less architecture, and distributed
security models. We propose practical guidelines including deep Q-learning and federated learning-based
transceivers, blockchain-based secure business models, homomorphic encryption, and distributed-ledger-
based authentication schemes to cope with these challenges. Finally, we outline and recommend several
future directions.
INDEX TERMS 6G, 5G, Internet of Things, Internet of everything, federated learning, meta learning,
blockchain.
I. INTRODUCTION
The remarkable upsurge of Internet of everything (IoE)-based
smart applications has paved the way for the evolution of
existing wireless networks. The term IoE refers to bringing
together things, data, people, and process, via emerging
technologies to offer a wide variety of smart services [1]. The
emerging IoE services include autonomous connected vehi-
cles, brain–computer interfaces, extended reality (XR), flying
vehicles, and haptics [2]–[4]. These services are mostly based
on ultra-high reliability, high data rates, unmanned mobil-
ity management, and long-distance communication. Fifth-
generation (5G) wireless networks are envisioned to enable
a wide variety of smart IoE-based services. The 5G targeted
tactile network is accessed via different approaches, such as
simultaneous use of unlicensed and licensed bands, intelli-
gent spectrum management, and 5G new radio, to enable
different smart applications [5]–[8]. However, 5G has several
inherent limitations and difficulties to completely fulfill its
target goals until now. The development of different data-
centric, automated processes are proving to exceed the capa-
bilities defined by key performance indicators of 5G [9]. For
instance, several applications, such as haptics, telemedicine,
and connected autonomous vehicles, are intended to use long
packets with ultra-high reliability and high data rates. Such
applications violate the notion of generally using short pack-
ets for ultra-reliable low-latency communication (URLLC)
in 5G [2]. The next generation of virtual and augmented
reality-based applications, such as holographic teleportation
VOLUME 4, 2016 1
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10.1109/ACCESS.2020.3015289, IEEE Access
Khan et al.: 6G Wireless Systems: A Vision, Architectural Elements, and Future Directions
6G UL Data Rate > 1 Tb/s
DL Data Rate > 1 Tb/s
Latency < 1 ms
Reliability = 1-10-9
frame error rate
Bio-Internet of Things
Connected
Autonomous
Vehicles
Blockchain
Visible Light
Communication
Terahertz Communication
Quantum Computing
Edge AI
Dataset
Haptics Communication
3D Networks
Extended Reality
Cell-less Architecture
Global
Model Local Models
Aggregation
Edge-based Federated Learning Model
Federated Learning
Quantum Machine
Learning Telemedicine
+
Quantum
Information
Processing
Quantum
Machine
Learning
Machine
Learning
Quantum
Computing
FIGURE 1: 6G wireless systems overview.
will require microsecond-level latency and Tbps-level data
rates [10]. Such a type of requirements seem difficult to be
fulfilled by 5G networks. Furthermore, the 5G connectivity
density of 106/km2[11] might not be able to meet the grow-
ing demands of next-generation smart industries. Therefore,
sixth-generation (6G) wireless systems must be developed.
6G will use artificial intelligence (AI) as an integral part
that has the capability to optimize a variety of wireless
network problems [12]. Typically, mathematical optimization
techniques are used to optimize wireless network problems.
To solve these mathematical optimization problems, we can
use convex optimization schemes, matching theory, game
theory, heuristic, and brute force algorithms. However, these
solution approaches might suffer from the issue of high
complexity which in turn degrades the capacity of a system.
Machine learning is capable of optimizing various complex
mathematical problems including the problems that cannot
be modeled using mathematical equations.
Rethink Technology Ltd. Research indicates several
challenges in the deployment of advanced wireless network-
ing technologies: guaranteed robustness, and management
and pricing of virtual network functions due to their uncertain
nature [13]. Other main problems include fronthaul cost issue
and vendor hostility. Although an open interface between
remote radio unit and baseband unit has been proposed, the
true evolved common public radio interface is difficult to
experience. Therefore, a novel 6G architecture must be devel-
oped to tackle these challenges. Fig. 1 presents an overview
of 6G wireless system and illustrates its key requirements
in terms of capacity, uplink data rate, downlink data rate,
localization precision, reliability in terms of frame error
rate, latency, jitter, and energy per bit [14]. Several enabling
technologies and use cases are also illustrated. Furthermore,
overview of different wireless mobile technologies with their
commencement year and other features is presented in Fig. 2
[15]. On the other hand, comparison of 5G and 6G for
different parameters is given in Table 1 [2], [14], [16].
2VOLUME 4, 2016
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Khan et al.: 6G Wireless Systems: A Vision, Architectural Elements, and Future Directions
1G
FDMA
Data rates: 2.4kbps
Voice service
2G
TDMA/FDMA
Data rates: 200kbps
Voice service
Data service
3G
WCDMA
Data rates: 30Mbps
Voice service
Data service
Video call
4G
OFDMA
Data rates: 1Gbps
Voice service
Data service
Video call
DVB
HD TV
5G
Data rates: up to 20Gbps
Voice service
Data service
Video call
DVB
HD TV
AR/VR
Smart City
1980 1992 2000 2010 2020
6G
Data rates: greater than 1Tbps
Voice service
Data service
Video call
DVB
HD TV
AR/VR/XR
IoE
AI-enabled Smart City
Edge AI
Blockchain
2030 (Expected)
FIGURE 2: Evolution of wireless mobile technologies.
TABLE 1: Comparison of 5G and 6G.
Parameter 5G6G
Peak data rate 10 20 Gb/s > 1Tbps
Spectrum efficiency 35x relative
to 4G
>3x relative to 5G
Receiver sensitivity About
120dBm
<130dBm
Latency ms level < 1ms
Mobility 350 km/h >1000km/h
Traffic density 10T b/s/km2>100T b/s/km2
Energy efficiency 1000x relative
to 4G
10x relative to 5G
Processing delay 100ns 10ns
End-to-end reliability
requirements
99.999 percent 99.99999 percent
Radio only delay re-
quirements
100ns 10ns
A. 6G MARKET STATISTICS AND RESEARCH
ACTIVITIES
Although 5G wireless systems are not fully deployed yet,
6G wireless systems are envisioned by much research to
fulfill the needs of expected novel IoE smart services in the
foreseeable future. According to statistics, the 6G market will
grow at a compound annual growth rate of 70% from 2025 to
2030 and reach 4.1billion US dollars by 2030 [17]. Among
various components of 6G, such as edge computing, cloud
computing, and AI, communication infrastructure will offer
the largest market share of up to 1billion US dollars. Another
key component of 6G; namely, AI chipsets, will be more than
240 million units in number by 2028.
Different organizations have started 6G projects [18]–
[21]. The 6G Flagship research program [18] is supported by
the Academy of Finland and led by the University of Oulu to
carry out the co-creation of an ecosystem for 6G innovation
and 5G adoption. The vision of the 6G flagship program is
a data-driven society with unlimited, instant wireless con-
nectivity. Initially, five organizations; namely, VTT Technical
Research Center of Finland Ltd., Oulu University of Applied
Sciences, Nokia, Business Oulu, and Aalto University, joined
the program as collaborators. Later, InterDigital and Keysight
Technologies joined the program. An agreement was signed
between the South Korean government and the University of
Oulu, Finland for the development of 6G technology [19].
Furthermore, LG has established its first research laboratory
at the Korea Advanced Institute of Science and Technology
to carry out 6G research activities [20]. SK Telecom started
joint research on 6G with Samsung, Nokia, and Ericsson
[21]. Research on 6G has started in China, as officially
announced by the Ministry of Science and Technology [22].
Moreover, the Chinese vendor Huawei has already started
6G research at its research center in Ottawa, Canada [23].
Several 6G research programs have been started in the US,
as announced by the US president [24]. Additionally, the
NYU WIRELESS research center, which comprises nearly
100 faculty members and graduate students, is working
on communication foundations, machine learning, quantum
nanodevices, and 6G testbeds [25]. Existing 6G tutorials and
surveys are discussed next.
B. EXISTING SURVEYS AND TUTORIALS
Several studies surveyed 6G wireless systems [2], [4], [9],
[10], [13], [16], [26]–[29]. Saad et al. presented applications,
enabling technologies, and few open research challenges [2].
They discussed applications, metrics, and new services for
6G. Moreover, 6G driving trends and performance matrices
were presented. Letaief et al. presented the vision of AI-
empowered 6G wireless networks [4]. They discussed 6G
network architecture with key enablers and 6G applications
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Khan et al.: 6G Wireless Systems: A Vision, Architectural Elements, and Future Directions
TABLE 2: Summary of the existing surveys and tutorials with their primary focus.
Reference Recent ad-
vances
Taxonomy Key enabling
technologies
Use cases Remarks
Saad et al., [2] 7 7 3 3 The tutorial presents applications,
enabling technologies, and few
open research challenges.
Letaief et al., [4] 7 7 3 3 The tutorial presents vision, net-
work architecture, and key enablers
for 6G.
Tariq et al., [26] 7 7 3 3 The tutorial discusses use cases, en-
abling technologies, and open re-
search challenges.
Giordani et al., [9] 7 7 3 3 The tutorial presents primarily 6G
key enabling technologies.
Zong et al., [13] 7 7 3 7 The tutorial mainly discusses en-
abling technologies, architecture,
requirements, and key drivers of
6G.
Yang et al., [27] 7 7 3 7 The tutorial provides an overview
of 6G based on time-frequency-
space resource utilization and key
enabling technologies.
Zhang et al., [28] 7 7 3 3 The tutorial mainly focuses on key
enabling technologies and vision of
6G.
Kato et al., [29] 7 7 7 7 The tutorial mainly focuses on chal-
lenges regarding machine learning
toward 6G.
Akyildiz et al., [10] 7 7 3 3 The survey comprehensively dis-
cuses enabling technologies, use
cases, physical layer modeling, and
open research challenges.
Chen et al., [16] 7 7 3 7 The tutorial presents requirements,
applications, technology trends,
and open research challenges
regarding 6G.
Our Survey 3 3 3 3 N.A
for various AI-enabled smart services. The authors in [26]
focused on 6G use cases, enabling technologies, and open
research challenges. The study conducted in [9] discussed
the evolution of wireless communication systems towards
6G and presented its use cases. Primarily, the authors pre-
sented 6G key enabling technologies with their associated
challenges and possible applications. Finally, they discussed
the integration of intelligence in 6G systems. Another study
discussed enabling technologies, architecture, requirements,
and key drivers of 6G [13]. The authors in [27] surveyed
potential technologies for 6G wireless networks. First, the au-
thors provided an overview of 6G based on time-frequency-
space resource utilization. Second, key techniques for the
evolution of wireless networks to 6G are presented. Finally,
the authors presented future issues regarding 6G deployment.
Zhang et al. mainly discussed the key technologies and vision
of 6G [28]. The authors discussed the 6G use cases and their
requirements in terms of peak data rate, user-experienced
data rate, over-the-air latency, energy efficiency, and connec-
tivity density. Kato et al. discussed various machine learning
schemes and presented 10 challenges regarding intelligen-
tization of 6G wireless systems [29]. Akyildiz et al. [10]
presented detailed discussions on key enabling technologies
for 6G. They presented key performance indicators and use
cases of 6G. Terahertz communications with its applications,
devices, physical layer modeling, and open problems are
discussed in detail. Furthermore, intelligent communication
environments with its layered architecture are described.
Finally, several open research challenges are presented. Chen
et al. presented vision, requirements, applications, and tech-
nology trends in 6G [16]. Furthermore, they discussed open
research challenges and orbital angular momentum (OAM)
as new resource for modulation in 6G.
C. OUR SURVEY
The work presented in [2], [4], [9], [10], [13], [16], [26]–[29]
focused on key enabling technologies, requirements, and use
cases of 6G. By contrast, we are the first to discuss state-
of-the-art advances and taxonomy for 6G wireless systems
to the best of our knowledge, as given in Table 2. We also
present novel open research challenges and future research
directions.
Our contributions are as follows:
We explore and discuss state-of-the-art advances made
toward enabling 6G systems.
We devise a taxonomy of 6G wireless systems based
on key enablers, use cases, emerging machine learn-
4VOLUME 4, 2016
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10.1109/ACCESS.2020.3015289, IEEE Access
Khan et al.: 6G Wireless Systems: A Vision, Architectural Elements, and Future Directions
ing schemes, communication technologies, networking
technologies, and computing technologies.
We discuss several open research challenges and their
possible solutions.
We provide an outlook for future research.
The rest of our survey is organized as follows. Section II
presents state-of-the-art advances toward enabling 6G sys-
tems. Moreover, a summary of features and merits with crit-
ical discussions is provided. Section III presents the devised
taxonomy using key enablers, use cases, emerging machine
learning schemes, communication technologies, networking
technologies, and computing technologies as parameters.
Open research challenges with guidelines are presented in
Section IV. Section V presents potential future research
directions and finally, paper is concluded in Section VI.
II. 6G:STATE-OF-THE-ART
This section presents state-of-the-art advances that enable
6G, as summarized in Table 3.
Khan et al. reviewed federated learning at the network
edge [30]. Resource optimization and incentive mechanism
design for federated learning at the network edge was consid-
ered. First, key design aspects for enabling federated learning
at the network edge were presented. These key design as-
pects are resource optimization, incentive mechanism design,
learning algorithm design, and hardware–software co-design.
Second, a Stackelberg-game-based incentive mechanism was
proposed. Additionally, a few numerical results were pre-
sented to validate their Stackelberg-game-based incentive
mechanism. Finally, several open research challenges and fu-
ture research directions were presented. Although the Stack-
elberg game-based incentive mechanism provides reasonable
results, it is recommended to further propose contract theory-
based incentive mechanism.
Wang et al. proposed a framework; namely, In-Edge AI,
to enable intelligent edge computing and caching via ma-
chine learning [31]. Deep Q-learning agents are placed at the
edge nodes in the proposed framework to offer intelligence.
An improved version of deep Q-learning; namely, double
deep Q-learning network (DDQN), was used in the paper
for two cases of edge caching and computational offload-
ing. Centralized DDQN and federated learning-based DDQN
were proposed to train the DDQN. Although federated-
learning-based DDQN has a generally slightly degraded per-
formance than centralized DDQN, it offers a substantially
lower consumption of communication resources for training
the learning agent at the network edge. The In-Edge AI
framework showed promising results for caching and edge
computing, there is a need to propose an incentive mecha-
nism and business model for the proposed framework. The
In-Edge AI framework has a large number of mobile users,
service providers, and different operators. Therefore, en-
abling their successful interaction requires effective incentive
mechanism design. Stackelberg game and contract theory-
based incentive mechanisms can be proposed for successful
interaction between a variety of players.
Mozaffari et al. introduced the concept of 3D cellu-
lar networks mainly based on drones [33]. They integrated
cellular-connected drone users with drone base stations con-
sidering the two problems of 3D cell association and network
planning. They introduced a new scheme using truncated
octahedron cells to compute the minimum number of drone
base stations and their feasible locations in a 3D space. They
also derived an analytical expression for frequency planning.
Finally, they presented an optimal-latency-aware 3D cell
association scheme. Mumtaz et al. provided an overview of
challenges and opportunities for terahertz communication in
vehicular networks [34]. They discussed different available
bands in the terahertz communication range. The authors
discussed different standardization activities regarding ter-
ahertz communication. However, 6G is currently in initial
phases and significant efforts are needed to turn its vision
of 6G using terahertz band into reality. There is a need to
define novel standards for 6G to incorporate terahertz com-
munication in addition to other emerging communication and
computing technologies. Nawaz et al. presented the vision of
quantum machine learning for 6G [32]. The authors reviewed
state-of-the-art machine learning techniques intended for use
in next-generation communication networks. Moreover, they
discussed state-of-the-art quantum communication schemes
and few open research challenges, and proposed a quantum-
computing-assisted machine learning framework for 6G net-
works. Finally, they discussed open research issues related to
the implementation of quantum machine learning in 6G.
Salem et al. considered the nanosensor network using
blood as a medium for terahertz communication to enable
smart healthcare applications [35]. They proposed an elec-
tromagnetic model for blood using effective medium theory.
An advantage of the proposed model is the flexibility of
specifying red blood cell volume fraction and particle shape.
Another advantage of their work is finding the relation of
molecular noise and path loss with the concentration of red
blood cells. Molecular noise and path loss decrease with an
increase in concentration of red blood cells, and vice versa.
Finally, the authors concluded that the particle shape of red
blood cells has no effect on blood, although it is considered a
medium for terahertz communication.
Carrasco et al. [36] proposed an architecture us-
ing terahertz communication for hierarchical body area
nanonetworks. They conceptually designed two kinds of
devices for the proposed architecture; namely, nanonodes and
nanorouters. They proposed a novel communication scheme
to enable communication between nanonodes using the ter-
ahertz band. They carried out communications using the
human hand and mitigated molecular absorption noise and
path loss. Another advantage of the proposed architecture is
coping with the issue of the decrease in transmission rate due
to energy limitations. They proposed using energy harvesting
from the blood stream and external sources to improve trans-
mission rate. Although the proposed architecture for body
area nanonetworks offers significant advantages and mainly
considered communication between nano-router and nano-
VOLUME 4, 2016 5
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2020.3015289, IEEE Access
Khan et al.: 6G Wireless Systems: A Vision, Architectural Elements, and Future Directions
TABLE 3: Summary of state-of-the-art.
Reference Category Feature Merit
Khan et al. [30] Edge AI
Resource optimization in federated learning at
network edge is considered
Incentive mechanism based on Stackelberg
game is proposed
Key design aspects of federated learning are
presented.
Novel open research challenges with possible
solutions are presented.
Few recommendations for future research are
presented.
Wang et al. [31] Edge AI
Intelligent edge caching and computation of-
floading are considered.
An In-Edge AI framework is proposed
Double deep Q-learning networks are used for
better performance
DDQN with federated learning is used to re-
duce the transmission overhead during the
training process.
Nawaz et al. [32] Quantum
machine learning Reviewed quantum machine learning for 6G
wireless networks
Proposed a framework for quantum machine
learning in 6G wireless networks
Presented the key research challenges for im-
plementation of quantum machine learning in
6G wireless networks
Mozaffari et al.
[33]
3D networking
3D cellular networks are introduced
3D networking planning and association are
proposed.
Proposed a latency-minimal scheme for associ-
ation between the drone base station and drone
user.
For feasible integer frequency reuse factors in
3D networks, an analytical expression is de-
rived.
The proposed 3D association scheme results
in significant latency minimization to serve the
drone users compared to traditional cellular
association schemes.
Mumtaz et al.
[34]
Terahertz
communication Terahertz communication for vehicular net-
works is considered
Provided bandwidth analysis of available bands
for terahertz communication.
Challenges and opportunities for terahertz com-
munication for next generation vehicular com-
munication networks are presented.
Salem et al. [35] Terahertz
communication Considered nano-sensor networks using tera-
hertz communication for health-care applica-
tions.
Presented an electromagnetic model based on
effective medium theory for blood.
The proposed model offers flexibility of speci-
fying red blood cells volume fraction and parti-
cle shape.
The authors found that both molecular noise
and path loss decreases with an increase in
concentration of red blood cells and vice versa.
Carrasco et
al.[36]
Terahertz
communication Proposed an architecture for hierarchical body
area nano-networks.
Two types of nano devices, such as nano-router
and nano-nodes are conceptually designed for
the proposed architecture using available elec-
tronic components.
They proposed a scheme for communication
between nano devices using terahertz band.
The impact of molecular absorption noise and
path loss on electromagnetic waves propagation
is mitigated.
Energy management has been performed for
low-energy nano devices.
nodes, it is preferable to analyze the communication model
between the external devices and body area nanonetworks to
enable dispatching of sensor data to end-users.
III. TAXONOMY
We consider key enablers, emerging machine learning
schemes, communication technologies, networking technolo-
gies, and computing technologies, to devise the taxonomy,
as shown in Fig. 3. Further discussion is provided in the
following subsections.
A. KEY ENABLERS
A6G system will use a wide variety of computing, com-
munication, networking, and sensing technologies to offer
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2020.3015289, IEEE Access
Khan et al.: 6G Wireless Systems: A Vision, Architectural Elements, and Future Directions
Emerging Machine
Learning Schemes
Federated
Learning
Meta Learning
Single Task
Multi-task
Key Enablers
Edge
Intelligence
Homomorphic
Encryption
Network
Slicing
Blockchain
AI and
Photonics-
Based
Cognitive Radio
Communication
Technologies
Terahertz
Communication
Quantum
Communication
3D Wireless
Communication
Visible Light
Communication
Nanoscale
Communication
Networking
Technologies
3D Networking
Nano
Networking
Bio Networking
Optical
Networking
Computing
Technologies
Quantum
Computing
High
Performance
Computing
Intelligent Edge
Computing
Quantum
Machine
Learning
Use Cases
Haptics
Communication
Unmanned
Mobility
Massive
URLLC
Human-Centric
Services
Holographic
Communication
based Services
Taxonomy
Ubiquitous
Sensing Bio-Internet of
Things
Nano-Internet
of Things
Space-Air-
Ground
Integrated
Network
FIGURE 3: Taxonomy of 6G wireless systems.
different novel smart applications. The key enablers of 6G
wireless systems are edge intelligence, homomorphic en-
cryption, blockchain, network slicing, AI, photonics-based
cognitive radio, and space-air-ground-integrated network.
Although network slicing was proposed in 5G as a key en-
abling networking technology, its true realization is expected
in 6G. Network slicing based on software-defined network-
ing (SDN) and network function virtualization (NFV) em-
ploys shared physical resources to enable slices of different
applications. The process of network slicing involves the
optimization of a variety of network parameters. One way
is to model them using mathematical optimization problem
that can be solved using different schemes, such as convex
optimization schemes, game theory, and iterative schemes.
However, mostly the later schemes are highly complex.
Therefore, there is a need to propose new solutions (e.g.,
machine learning-based solutions) with low complexity. Over
2,000 configurable parameters are expected in a typical 6G
smart device [31]. Therefore, using smart devices based
on effective machine learning schemes is indispensable.
Photonics-based cognitive radio assisted by machine learning
will enable intelligence in 6G radio and offer features of
scalability, ultra-reliability, low latency, and ultra-broadband
features.
Blockchain is a distributed ledger that will enable se-
cure, robust exchange of data among smart citizens [37]–
[39]. Therefore, it can be considered one of the key tech-
nologies for 6G to enable smart supply chain, smart grid,
and smart healthcare [40]–[42]. Although blockchain will
be considered to be one of the key enabling technologies of
6G systems, it has few challenges. Mainly, these challenges
are simultaneous scalability and reliability, high-latency, and
high energy consumption for running consensus algorithm
[43], [44]. 6G systems are envisioned mainly to enable
extremely low latency (i.e., less than 1ms), low energy
consumption (i.e., 1pJ/b), and reliability (i.e., 1109).
Therefore, significant efforts for designing blockchain with
low-latency, ultra-high scalability and reliability, are required
to truly enable its existence into 6G. On the other hand,
ubiquitous sensing involves machine vision and 3D-range
imaging using video-captured information to enable sensing
automation and smart decision making [45], [46]. Ubiq-
uitous sensing will serve as a key technology of smart
cyber–physical systems for enabling novel 6G applications
[47]. Space-air-ground integrated network (SAGIN) consists
of terrestrial communication networks, aerial networks, and
satellite networks, which can be considered to one of the key
enablers of 6G [2]. One of the many advantages of SAGIN is
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Khan et al.: 6G Wireless Systems: A Vision, Architectural Elements, and Future Directions
to provide coverage to scarce infrastructure areas by drones-
based BSs. Other advantages of SAGIN are strong resilience,
high throughput, and large coverage [48]. Although SAGIN
offers several benefits, it suffers from the challenge of how
to effectively perform end-to-end quality of service manage-
ment, mobility management, load balancing, power control,
and spectrum allocation, among all network segments. There-
fore, we must design novel schemes for SAGIN-enabled
6G to enable optimal end-to-end performance among all the
network segments.
B. USE CASES
Although 5G wireless networks were conceived to provide a
wide variety of smart services, several services disrupt the
vision of 5G design. Generally, 5G use cases have three
main classes, such as URLLC, enhanced mobile broad-
band (eMBB), and massive machine-type communication
(mMTC). However, several new applications are disrupting
the vision of 5G use cases and we need new use cases. For
instance, consider XR (i.e., combining mixed reality, aug-
mented reality, and virtual reality [49]) and brain-computer
interaction that requires 5G-eMBB high data rates, low-
latency, and high reliability. Therefore, we must define new
use cases for these emerging applications. The novel 6G ser-
vices are haptics, autonomous connected vehicles, massive
URLLC (mURLLC), human-centric services, bioInternet of
things (B-IoT), nanoInternet of things (N-IoT), and mobile
broadband reliable, low-latency communication [2], [9], [28].
Novel 6G use cases are provided below:
Massive URLLC: mURLLC denotes IoE applications
based on application-dependent scaling of classical
URLLC [2]. mURLLC will be based on merging mas-
sive machine-type communication and 5G URLLC.
This use case will offer a trade-off between reliabil-
ity, scalability, and latency. Examples of mURLLC are
smart factories and smart grids, which require ultra-
reliability and low-latency communication. In addition,
we expect a massive number (more than 106/km2) of
nodes for cyber-physical system-enabled smart factories
and smart grids in the future [10]. Therefore, we must
scale the classical 5G URLLC to a massive URLLC to
meet the requirements of these new applications.
Human-centric services: Although 5G offers numer-
ous advantages, such as basic augmented and virtual
reality services, high-definition video streaming, inter-
net protocol television, among others, there is a need
to propose services that are more human-centric. In
contrast to 5G use cases, the human-centric services use
case represents a service that is intended to fulfill new
user-centric metrics (i.e., quality of physical experience)
[2], [50]. A common example is brain–computer inter-
face whose performance can be measured via human
physiology.
Haptics communication: Haptic communication, a
form of non-verbal communication, deals with enabling
sense of touch from a remote place [51]. However,
enabling this type of real-time interactive experience
using 6G requires substantial design efforts.
Holographic communication based services: This use
case is based on a remote connection with an ultra-
high accuracy [9]. Holographic communication will be
based on multiple-view camera image communication
that requires substantially higher data rates (Tbps) [14].
Unmanned Mobility: This use case deals with fully
autonomous connected vehicles that offer complete un-
manned mobility, safe driving, smart infotainment, and
enhanced traffic management [9].
Nano-Internet of Things: N-IoT uses nanodevices for
communication over a network. For instance, nanocom-
munication in a smart factory can be used to monitor
carbon emissions, water quality, gaseous fumes, and
humidity. As N-IoT mainly uses molecular communica-
tion which seems difficult to enable by 5G. We should
consider 6G for molecular communication-based N-
IoT [52]. Nano-networks use terahertz band for better
performance [10], which falls in 6G [2]. Therefore, we
can say that N-IoT can be better enabled by 6G. The
key requirements for N-IoT must be specified based
on 6G because N-IoT is in its infancy. The N-IoT
has several implementation challenges, such as physi-
cal layer schemes for macro and micro-scale molecu-
lar communication (i.e., detection and channel estima-
tion), standardization of layered architecture, design of
nano-things, and development of application-oriented
testbeds.
Bio-Intenet of Things: : B-IoT is based on the commu-
nication of biodevices (nanobiological devices) using
IoT. This use case represents the variety of smart health-
care applications using biocommunication. Similar to
N-IoT, the key performance requirements for B-IoT
must be specified. The works in [35] and [36] used
the terahertz band, which is one of the key enablers of
6G. Therefore, we can say that B-IoT can be effectively
enabled by 6G.
C. EMERGING MACHINE LEARNING SCHEMES
Machine learning (ML) is considered one of the key drivers
of 6G. ML recently elicited great attention in enabling nu-
merous smart applications. In 6G, ML is expected to not
only enable smart applications but also provide intelligent
medium access control schemes and intelligent transceivers
[29], [53], [54].Thus, ML can be one of the fundamental
pillars of the 6G wireless network. Generally, we can divide
ML into several types: traditional machine learning, feder-
ated learning, meta learning, and quantum machine learning.
Traditional machine learning is based on the migration of
data from end devices to a centralized server for training
the machine learning model. However, this approach suffers
from issues of privacy concerns and high overhead in the
migration of data to a centralized server [55]. Furthermore,
centralized machine learning generally suffers from high
power consumption during the training process for large
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Khan et al.: 6G Wireless Systems: A Vision, Architectural Elements, and Future Directions
datasets. Coping with this issue, one can use distributed
machine learning. Distributed machine learning can lead to
high-performance computing by enabling parallel compu-
tation of machine learning models at distributed locations
[56]. There can be two possible ways, such as the data-
parallel approach and model-parallel approach, to distribute
the machine learning tasks. The data-parallel approach is
based on a division of data among nodes, with all nodes
running the same machine learning model. On the other hand,
the model-parallel approach is based on training portions
of the machine learning model which are distributed across
many nodes, with every node having an exact copy of the
data. However, this approach might not be feasible for many
machine learning models that cannot be split up into parts.
To deploy distributed machine learning models using
data-parallel approach, there can be many possible ways.
These ways include centralized, tree-based decentralized,
parameter server-based decentralized, and fully distributed
[56]. In a centralized ensemble-based distributed learning, a
strict aggregation fashion at one central location is adopted.
Tree-based decentralized learning allows intermediate ag-
gregation at child nodes before a central aggregation takes
place [57]. Parameter server-based decentralized learning is
based on storing all the client’s updates on a shared param-
eter server. In case of fully distributed, all nodes directly
communicate with each other for the model sharing. On
the other hand, federated learning can be considered as
a special type of distributed machine learning. Federated
learning was recently adopted for edge networks to tackle
these prominent challenges of traditional machine learning
[30], [31]. Federated learning enables machine learning in a
distributed manner by enabling on-device machine learning
without migrating data from end devices to the edge/cloud
server. However, federated learning has its inherent chal-
lenges including communication and computation resource
optimization, incentive mechanism design, and local device
learning algorithm design. Quantum machine learning com-
bines quantum physics and machine learning to enable fast
training of machine learning models. Meta learning enables
the machine learning models to learn, but has complexity
in design because various machine learning models have
different natures.
D. COMMUNICATION TECHNOLOGIES
A6G system will use novel communication technologies
to enable various smart applications. These communication
technologies are terahertz communication, quantum commu-
nication, 3D wireless communication, visible light communi-
cation, nanoscale communication, and holographic commu-
nication. Recently, 3GPP has developed a new radio access
technology; namely, 5G new radio using sub-6GHz and
mmWave bands for enabling high data rates [58]. To enable
further higher data rates, 6G will use terahertz bands in addi-
tion to mmWave bands. Generally, terahertz communication
uses frequencies from 0.1to 10 terahertz and is characterized
by short-range, medium-power consumption, high security,
and robustness to weather conditions [59]–[61]. Terahertz
communication offers several advantages, but several chal-
lenges must be resolved to enable its use in 6G. These
challenges involve the design of efficient transceivers with
advanced adaptive array technologies to increase its range.
Another important aspect of 6G is the use of 3D communica-
tion which involves the integration of ground and airborne
networks. Unarmed aerial vehicles and low-orbit satellites
can be used as base stations for 3D communication [62]. In
contrast to 2D (ground) communication, 3D communication
has a substantially different nature because of the introduc-
tion of altitude dimension. Therefore, novel schemes are
necessary for resource allocation and mobility handling for
3D communication networks. Nanoscale communication is a
new communication technology that uses an extremely short
wavelength for communication and is suitable for a distance
of 1 m or cm. Key challenges of nanoscale communication
are nanoscale transceiver design and channel modeling.
Visible light communication can be used to enable sev-
eral 6G applications using a visible light spectrum that ranges
from 430 THz to 790 THz [63], [64]. The main advantage
of visible light communication is the use of illumination
sources for lighting and communication. Moreover, visible
light communication offers a substantial large bandwidth
and interference-free communication from radio frequency
waves. However, visible light communication with low-
range, novel transceivers (acting as illumination source and
communication source) must be designed to enable differ-
ent visible light communication-based applications. Further-
more, several other challenges must be resolved to enable 6G
with visible light communication. Such challenges include
connectivity of light-emitting diode to the Internet, inter-
cell interference, mobility and coverage, among others [63].
To enable 6G with a high capacity, one can deploy light-
emitting diodes for visible light communication in a dense
fashion. However, it will suffer from inter-cell interference
which must be given proper attention. To enable seamless
connectivity to users using visible light communication, it is
essential to the handle mobility problem. In a typical visible
light communication cell, there exists significant variations
in signal-to-interference-ratio. Therefore, we must effectively
handle the mobility issues using visible light communication.
Quantum communication has the inherent feature of high
security, which makes it preferred for 6G [65], [66]. The
simultaneous achievement of long-distance and high rates
is contradictory in quantum communication [67]. There-
fore, repeaters must be used to enable secure long-distance,
high-data-rate quantum communication. However, current
repeaters cannot be used for quantum communication, and
new repeaters must be designed.
E. NETWORKING TECHNOLOGIES
Novel networking technologies for 6G are nanonetworking,
bionetworking, optical networking, and 3D networking [68].
The operation of the N-IoT is based on molecular com-
munication. Different materials, such as graphene and meta
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Khan et al.: 6G Wireless Systems: A Vision, Architectural Elements, and Future Directions
materials can be used to build nanometer-range devices. B-
IoT using biological cells are used for communication using
IoT [69], [70]. B-IoT and N-IoT are seemingly integral parts
of future 6G smart services but have several implementation
challenges. The design of physical layer technologies for
molecular communication is a challenging task. Apart from
physical layer techniques, novel routing schemes must be
proposed because of the substantially different nature of
B-IoT and N-IoT compared with traditional IoT. Efficient
nanodevices and biodevices must be developed for N-IoT and
B-IoT because they are in infancy. However, 3D networking
uses drone-based user devices and drone-based base stations
to enable communication networks. Thus, novel models must
be devised for a 3D communication network due to its
substantially different nature compared with a 2D network.
F. COMPUTING TECHNOLOGIES
A6G system involves a wide variety of sources of different
smart applications that generate an enormous amount of
data. High-performance computing and quantum computing
must be used to enable intelligent data analytics. Quantum
computing is expected to revolutionize the field of computing
by enabling higher speeds that users have never experienced
until now [71], [72]. The key feature of quantum commu-
nication is secure channels, where every channel carries its
distinct security protocols constructed into encrypted data.
These features of security in addition to ultra-high speed
make quantum computing preferable for secure 6G smart
applications. Other than quantum computing, intelligent edge
computing is required for 6G to provide intelligent on-
demand computing and on-demand storage capabilities with
extremely low latency to end nodes [73]–[77].
IV. OPEN RESEARCH CHALLENGES
We present several novel open research challenges for 6G.
Their causes and possible solutions are discussed and sum-
marized in Table 4.
A. AI-BASED ADAPTIVE TRANSCEIVERS
How do we enable a 6G transceiver with a large number
of intelligent, adaptive tunable parameters? A typical 6G
transceiver is expected to have numerous tunable param-
eters. These parameters can be adaptively tuned via ma-
chine learning algorithms. For instance, consider the training
of a deep Q-learning agent for intelligent caching in XR
applications. The Q-learning agent can be trained in two
ways: traditional machine learning and federated learning.
Traditional machine learning requires shifting of data from
end devices to the edge/cloud server for the training of the
deep Q-learning agent deployed at the edge/cloud server.
The sending of data from end devices to the edge server
has a substantial cost in terms of wireless communication
resources. By contrast, federated learning can be used to train
the deep Q-learning agent efficiently by reducing wireless
resource usage through sending only model updates (that
have much less size compared with the whole training data)
to the edge/cloud server. Similarly, federated learning can be
used to enable intelligence in an adaptive transceiver.
B. INTELLIGENT WIRELESS ENERGY HARVESTING
How do we enable 6G smart applications in a sustainable
fashion? Enabling 6G applications sustainably requires the
use of energy-efficient devices and renewable energy sources.
Wireless energy harvesting can be one of possible ways to
enable sustainable operation of 6G. Wireless energy harvest-
ing covers numerous harvesting scenarios: dedicated radio
frequency harvesting sources, interference-aware harvesting,
and ambient sunlight harvesting. However, substantial vari-
ations exist in harvested energy for these wireless energy-
harvesting sources. Therefore, an intelligent power control
must be developed for energy-harvesting devices. Traditional
power control schemes for energy-harvesting devices assume
the known system state (incoming harvesting energy and
wireless channel), but this information is not available prac-
tically. Machine learning can be used to predict the future
system state and address these challenges. Reinforcement
learning can be one of the possible solutions with unknown
statistical knowledge and observable current system state,
but it has a limitation of use in only finite system states.
Another approach to cope with this limitation is the use
of Lyapunov opportunistic optimization and online-learning-
based schemes [78].
C. DECENTRALIZED AND SECURE BUSINESS MODELS
How do we enable a wide variety of geographically dis-
tributed and diverse players in 6G to interact cost effectively
and securely? Novel decentralized, secure business models
must be designed to enable a cost-effective interaction among
various geographically distributed players in 6G economi-
cally and securely. A centralized business model will offer
high latency, which is undesirable for ultra-high-speed 6G
smart services. Therefore, new distributed business models
for 6G must be developed. Different schemes can be used for
security in business models. One of these schemes can be a
blockchain-based secure service brokering between suppliers
and providers.
D. INTELLIGENT CELL-LESS ARCHITECTURE
How do we enable intelligent management of a large vari-
ety of different communication technologies in the cell-less
architecture of 6G wireless systems? A 6G system will be
based on a true cell-less architecture to avoid handover issues
and offer seamless communication with improved quality
of experience to end users. Therefore, a novel architecture
for 6G enables a seamless interaction between numerous
communication technologies, such as visible light communi-
cation, millimeter-wave communication, and terahertz com-
munication. All access points/base stations of different com-
munication techniques should serve the users in collaboration
to improve the signal-to-noise-plus-interference ratio. Intelli-
gent operation of 6G can be enabled via intelligent cognitive
radio with self-sustaining, adaptive features. A software-
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Khan et al.: 6G Wireless Systems: A Vision, Architectural Elements, and Future Directions
TABLE 4: Summary of the research challenges and their guidelines
Challenges Causes Guidelines
AI-based adaptive
transceivers Large variety of tunable parameters
Significant variations in physical layers require-
ments for different applications
Deep Q-learning based intelligent transceiver
Federated learning based transceiver
Distributed and secure
business models Strict-latency 6G applications
Geographically distributed services providers over a
large area
Novel distributed business models
Blockchain based secure service brokering
Intelligent wireless en-
ergy harvesting Sustainable operation
Existence of a wide variety of interference signals
Reinforcement learning-based energy harvesting
schemes
Lyapunov optimization and online learning based
schemes
Intelligent cell-Less ar-
chitecture Management of several communication technolo-
gies with different features
Different communication bands with distinct fea-
tures
Deep Q-learning-based cognitive radio
Quantum machine learning based software-defined
cognitive radio
Distributed security
models Training of machine learning models at the network
edge
Requirement of distributed authentication with low-
latency for smart applications
Homomorphic encryption
Distributed-ledger based authentication schemes
Reconfigurable smart re-
flecting surfaces-enabled
6G
High path loss for millimeter wave and terahertz
communication.
Loss in capacity.
Deep learning-based reconfigurable smart reflecting
surface
Reconfigurable smart reflecting surface as an access
point
defined cognitive radio using machine learning can be used to
perform several intelligent operations: self-protection against
interference, self-fault recovery, self-optimization, and self-
management. One possible way to enable software-defined
cognitive radio is the use of deep Q-learning. Quantum
machine learning can also be used to enable fast learning of
machine learning models [32].
E. DISTRIBUTED SECURITY MODELS
How do we enable distributed machine learning and dis-
tributed computing for 6G while preserving user privacy?
A6G wireless system will use AI to enable different smart
applications and networking functions. Traditional machine
learning models migrate user data to the edge/cloud server
for training the learning model. Therefore, homomorphic
encryption, which enables sending of encrypted data to the
edge/cloud server rather than un-encrypted data, can be used
to address this type of privacy concern. A novel distributed
authentication scheme must be proposed for 6G wireless
systems. Distributed ledger technology (using blockchain)-
based authentication schemes can be one of the possible
solutions for 6G-distributed authentication.
F. RECONFIGURABLE SMART REFLECTING
SURFACES-ENABLED 6G
How do we enable 6G wireless systems with reconfigurable
smart reflecting surfaces to simultaneously improve through-
put and energy efficiency? To enable 6G with high ca-
pacity using millimeter-wave and terahertz communication,
we can use massive multiple-input-multiple-output (MIMO)
with antenna arrays for meeting increasing demands in ca-
pacity [79]. Although an increase in frequency reduces the
scattering and diffraction effect, it suffers from the block-
ing of electromagnetic waves by buildings. Additionally,
high-frequency communication suffers from significant path
loss. Coping with the aforementioned issues, we can use
reconfigurable smart reflecting surfaces. A typical recon-
figurable smart reflecting surface is comprised of several
reconfigurable reflecting elements that can reflect impinging
electromagnetic waves. In [80], Liaskos et al. used deep
learning-based reconfigurable smart surface to improve wire-
less communication performance. Another approach is to use
reconfigurable smart reflecting surface-based access points
[81], which involve sending of an unmodulated carrier signal
to smart surfaces with negligible fading by radio frequency
signal generator. The reconfigurable smart reflecting surface
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Khan et al.: 6G Wireless Systems: A Vision, Architectural Elements, and Future Directions
Scalable and Reliable
Blockchain-Enabled 6G
Adaptive Machine Learning-
Enabled 6G
Fairness-Enabled
Federated Learning Distributed Machine
Learning Low-Latency consensus
Algorithms Robust Consensus
Algorithms
6G Network-as-an-
Intelligent-Service
Routing Schemes for 6G-
Enabled N-IoT and B-IoT
Energy Efficient
Forwarding Low Computational
Complexity Network Slicing Intelligentization
Zero-Energy-Enabled 6G
Energy HarvestingRenewable Energy
Modeling for Terahertz and
Millimeter Wave
Communication
Physical Layer
Modeling
Network Layer
Modeling
Self-Sustaining 6G Networks
Adaptive Schemes
Machine Learning-
Enabled Schemes
Meta-Learning-Enabled 6G
Machine Learning
Model
Implementation
Machine Learning
Model Selection
6G Wireless Systems:
Future Directions
FIGURE 4: 6G wireless systems: future directions.
then uses phase shifts to convey the bits.
V. POTENTIAL FUTURE DIRECTIONS
We derive several future research directions (overview is
presented in Fig. 4) from the study as follows.
A. ADAPTIVE MACHINE LEARNING-ENABLED 6G
Machine learning can be considered an integral part of 6G,
but applying a specific type of machine learning technique for
6G must examine the application nature. For instance, con-
sider autonomous driving cars that generate 4,000 gigaoctet
of data every day [82]. In this scenario, real-time interaction
is necessary. Centralized machine learning based on one-
time training can be used. However, the model trained via
centralized machine learning might not produce good results
due to the frequent addition of new data. Therefore, federated
learning is preferred over centralized machine learning for
this type of scenario. Federated learning offers the advantage
of considering newly added data training but suffers from
fairness issues. q-fair federated learning was proposed to deal
with fairness issues [83]. In q-federated learning, local learn-
ing weights of devices with poor performance are given more
weight, and vice versa. Although q-fair federated learning
can enable efficient federated learning via adjusting weights,
it suffers from the challenge of how to dynamically adjust
weights. Therefore, centralized machine learning, where user
privacy has less importance and does not suffer from frequent
addition of data, can be used.
Another way is to use distributed learning based on
training a machine learning model using a dataset at a
centralized location. Machine learning model parameters are
then sent to the end devices. Finally, the end devices update
the global learning models using their local datasets. The
advantage of distributed machine learning is the one time-
sharing of learning model parameters between the centralized
server and end devices, thus avoiding resource fairness chal-
lenges. However, distributed learning needs the dataset at the
centralized location having sufficient data (might not be all
device data) from training, which again causes some privacy
leakage to a lesser extent than centralized machine learning.
B. SCALABLE AND RELIABLE BLOCKCHAIN-ENABLED
6G
Blockchain is a promising technology that offers secure
storage of transactions in a distributed, immutable ledger.
Various smart services that can be enabled by blockchain
are smart healthcare, smart supply chain management, smart
transportation, and smart property management. A 6G sys-
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Khan et al.: 6G Wireless Systems: A Vision, Architectural Elements, and Future Directions
tem is intended to provide enhanced scalability and relia-
bility, extremely low latency, and low energy consumption.
However, existing blockchain consensus algorithms might
pose limitations in terms of scalability, reliability, latency,
and energy consumption [84]. Implementation of blockchain
to achieve key design aspects, such as fault tolerance, secu-
rity, low latency, and decentralization simultaneously poses
substantial challenges on scalability and reliability, which is
one of the primary goals of 6G systems [2], [85]. A novel
consensus algorithm that offers enhanced reliability and scal-
ability while providing tradeoffs between fault tolerance,
security, and latency must be proposed to benefit from the
deployment of blockchain in 6G systems.
C. 6G NETWORK-AS-AN-INTELLIGENT-SERVICE
A5G network was envisioned to enable numerous smart
services via transformation of network-as-an-infrastructure
to network-as-a-service. Network-as-a-service offers the use
of shared physical resources via network slicing to serve
different smart services [86]. Network slicing uses SDN
and NFV as key enablers. SDN offers separation of the
control plane from the data plane, thus offering efficient
network management [87]. NFV allows the cost-efficient
implementation of different networking functions on generic
hardware using virtual machines. Although network slicing
enables efficient resources usage while fulfilling end-user de-
mands, it might not perform well with an increase in network
heterogeneity and complexity [4].Therefore, network-as-a-
service must be transformed to network-as-an-intelligent-
service. Network intelligentization will enable 6G systems to
adjust various parameters adaptively, thus offering enhanced
performance.
D. SELF-SUSTAINING 6G NETWORKS
A self-organizing (i.e., self-operating) network offers opti-
mization, management, configuration, and planning in an
efficient, fast manner [88], [89]. Self-organizing network was
systematically outlined in 3GPP Release 8. However, the
traditional self-organizing networking scheme might not be
feasible for 6G systems due to the presence of a complex,
dynamic environment. Therefore, a novel, self-sustaining 6G
network architecture must be proposed [2]. Self-sustaining
6G systems must adapt to the highly dynamic environ-
ment sustainably. Furthermore, emerging machine learning
schemes must be used to enable efficient, self-sustainable 6G
systems.
E. MODELING FOR TERAHERTZ AND MILLIMETER
WAVE COMMUNICATION
We propose novel models (physical layer and networking
layer) for millimeter-wave and terahertz bands because of
their substantially different nature compared with existing
lower-frequency bands. For fixed nodes, terahertz commu-
nication has fewer challenges than mobile nodes [90]. There-
fore, we must propose novel schemes for terahertz commu-
nication in case of mobile nodes. Based on the new design
models, we can propose an optimization framework to enable
6G services according to their key performance indicators.
F. ZERO-ENERGY-ENABLED 6G
We recommend designing zero-energy 6G systems. A 6G
wireless communication system must use renewable energy
and radio-frequency-harvesting energy for its operation (i.e.,
hybrid energy sources). However, energy from the grid sta-
tion must be used when radio frequency harvesting energy
level fall below the required energy level for their operation.
The zero-energy wireless system must return the equivalent
amount of energy to the grid during the time of excess radio
frequency harvesting energy to account for the consumed
energy from the grid.
G. ROUTING SCHEMES FOR 6G-ENABLED N-IOT AND
B-IOT
N-IoT and B-IoT have a substantially different nature com-
pared with traditional IoT. Therefore, novel routing schemes
must be developed. Routing schemes with low computa-
tional complexity and short-range communication must be
proposed due to limited energy, short-range communication,
and low computing capabilities of nanonodes and bionodes.
Moreover, nanonetworks can operate in a terahertz band,
thus requiring substantial effort for routing protocol design
[91]. Therefore, novel routing schemes based on energy-
efficient forwarding and low computational complexity must
be proposed for N-Iot and B-IoT.
H. META-LEARNING-ENABLED 6G
Machine learning is considered an integral part of 6G. How-
ever, training the machine learning model by selecting ap-
propriate learning model parameters requires extensive ex-
perimentation. By contrast, meta learning provides machine
learning models the capability to learn. However, 6G smart
applications enabled by machine learning have a substan-
tially different nature. Therefore, we recommend novel meta
learning models to assist the learning of numerous machine
learning models by offering them appropriate learning model
parameters to enable different 6G smart applications. A two-
stage meta learning framework can be used to solve different
machine learning problems in 6G [92]. The first stage can
select the machine learning model, and the second stage will
implement the selected machine learning model.
VI. CONCLUSIONS
We have presented recent advances made toward enabling
6G wireless systems, proposed a comprehensive taxonomy
based on different parameters, and presented several open
challenges along with important guidelines. We conclude that
6G systems will unlock the full potential of smart cities via
enabling Internet of everything based smart services. AI will
be an integral part of the 6G wireless system to solve complex
network optimization problems. Terahertz communication
will be considered as one of the key communication bands
for 6G systems. It is essential to propose novel models for
VOLUME 4, 2016 13
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2020.3015289, IEEE Access
Khan et al.: 6G Wireless Systems: A Vision, Architectural Elements, and Future Directions
terahertz communication. Furthermore, new models must be
proposed for quantum communication that is currently in
infancy.
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LATIF U. KHAN is currently pursuing his Ph.D.
degree in Computer Engineering at Kyung Hee
University (KHU), South Korea. He is working as
a leading researcher in the intelligent Networking
Laboratory under a project jointly funded by the
prestigious Brain Korea 21st Century Plus and
Ministry of Science and ICT, South Korea. He
received his MS (Electrical Engineering) degree
with distinction from University of Engineering
and Technology (UET), Peshawar, Pakistan in
2017. Prior to joining the KHU, he has served as a faculty member and
research associate in the UET, Peshawar, Pakistan. He has published his
works in highly reputable conferences and journals. He has received the
best paper award in 15th IEEE International Conference on Advanced
Communications Technology, PyeongChang, South Korea, in 2013. His
research interests include analytical techniques of optimization and game
theory to edge computing and end-to-end network slicing.
IBRAR YAQOOB (S’16-M’18-SM’19) is a re-
search professor with the Department of Computer
Science and Engineering, Kyung Hee University,
South Korea, where he completed his postdoctoral
fellowship under the prestigious grant of Brain
Korea 21st Century Plus. Prior to that, he received
his Ph.D. (Computer Science) from the University
of Malaya, Malaysia, in 2017. He worked as a
researcher and developer at the Centre for Mobile
Cloud Computing Research (C4MCCR), Univer-
sity of Malaya. His numerous research articles are very famous and among
the most downloaded in top journals. He has reviewed over 200 times for
the top ISI- Indexed journals and conferences. He has been listed among top
researchers by Thomson Reuters (Web of Science) based on the number of
citations earned in last three years in six categories of Computer Science. He
is currently serving/served as a guest/associate editor in various Journals.
He has been involved in a number of conferences and workshops in various
capacities. His research interests include big data, edge computing, mobile
cloud computing, the Internet of Things, and computer networks.
MUHAMMAD IMRAN is an associate professor
at King Saud University. His research interest
includes MANET, WSNs, WBANs, M2M/IoT,
SDN, Security and privacy. He has published a
number of research papers in refereed interna-
tional conferences and journals. He served as a
Co-Editor in Chief for EAI Transactions and As-
sociate/Guest editor for IEEE (Access, Commu-
nications, Wireless Communications Magazine),
Future Generation Computer Systems, Computer
Networks, Sensors, IJDSN, JIT, WCMC, AHSWN, IET WSS, IJAACS and
IJITEE.
ZHU HAN (S’01, M’04, SM’09, F’14) received
the B.S. degree in electronic engineering from Ts-
inghua University, in 1997, and the M.S. and Ph.D.
degrees in electrical and computer engineering
from the University of Maryland, College Park, in
1999 and 2003, respectively. From 2000 to 2002,
he was an R&D Engineer of JDSU, Germantown,
Maryland. From 2003 to 2006, he was a Research
Associate at the University of Maryland. From
2006 to 2008, he was an assistant professor at
Boise State University, Idaho. Currently, he is a John and Rebecca Moores
Professor in the Electrical and Computer Engineering Department as well as
in the Computer Science Department at the University of Houston, Texas.
He is also a Chair professor in National Chiao Tung University, ROC.
His research interests include wireless resource allocation and management,
wireless communications and networking, game theory, big data analysis,
security, and smart grid. Dr. Han received an NSF Career Award in 2010,
the Fred W. Ellersick Prize of the IEEE Communication Society in 2011, the
EURASIP Best Paper Award for the Journal on Advances in Signal Process-
ing in 2015, IEEE Leonard G. Abraham Prize in the field of Communications
Systems (best paper award in IEEE JSAC) in 2016, and several best paper
awards in IEEE conferences. Dr. Han was an IEEE Communications Society
Distinguished Lecturer from 2015-2018 and is AAAS fellow since 2019
and ACM distinguished Member since 2019. Dr. Han is 1% highly cited
researcher since 2017 according to Web of Science.
16 VOLUME 4, 2016
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2020.3015289, IEEE Access
Khan et al.: 6G Wireless Systems: A Vision, Architectural Elements, and Future Directions
CHOONG SEON HONG (S’95-M’97-SM’11)
received the B.S. and M.S. degrees in electronic
engineering from Kyung Hee University, Seoul,
South Korea, in 1983 and 1985, respectively, and
the Ph.D. degree from Keio University, Minato,
Japan, in 1997. In 1988, he joined Korea Telecom,
where he worked on broadband networks as a
Member of Technical Staff. In September 1993,
he joined Keio University. He worked for the
Telecommunications Network Laboratory, Korea
Telecom, as a Senior Member of Technical Staff and the Director of the
Networking Research Team until August 1999. Since September 1999,
he has been a Professor with the Department of Computer Science and
Engineering, Kyung Hee University. His research interests include future
Internet, ad hoc networks, network management, and network security. He
is a member of ACM, IEICE, IPSJ, KIISE, KICS, KIPS, and OSIA. He
has served as the General Chair, a TPC Chair/Member, or an Organizing
Committee Member for international conferences such as NOMS, IM, AP-
NOMS, E2EMON, CCNC, ADSN, ICPP, DIM, WISA, BcN, TINA, SAINT,
and ICOIN. In addition, he is currently an Associate Editor of the IEEE
Transactions on Network and Service Management, International Journal of
Network Management, and Journal of Communications and Networks and
an Associate Technical Editor of the IEEE Communications Magazine.
VOLUME 4, 2016 17
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