# A Comprehensive Survey on 6G Networks: Applications, Core Services, Enabling
Technologies, and Future Challenges

Amin Shahraki*, Mahmoud Abbasi, Md. Jalil Piran*, and Amir Taherkordi Amin
Shahraki (Corresponding Author) is with the Department of Informatics,
University of Oslo, Oslo, Norway (e-mail: am.shahraki@ieee.org)Mahmoud Abbasi
is with Islamic Azad University, Mashhad, Iran (email:
mahmoud.abbasi@ieee.og)Md. Jalil Piran (Corresponding Author) is with the
Department of Computer Science and Engineering, Sejong University, Seoul,
05006, South Korea (email: piran@sejong.ac.kr)Amir Taherkordi is with
University of Oslo, Oslo, Norway (email: amirhost@ifi.uio.no)Manuscript
received xxx xx, 2021; revised xxx xx, 2021.

###### Abstract

Cellular Internet of Things (IoT) is considered as de facto paradigm to
improve the communication and computation systems. Cellular IoT connects
massive number of physical and virtual objects to the Internet using cellular
networks. The latest generation of cellular networks, e.g. fifth-generation
(5G), use evolutionary and revolutionary technologies to notably improve the
performance of wireless networks. However, given the envisioned new use-cases,
e.g., holographic communication, and the ever-increasing deployment of massive
smart-physical end-devices in IoT, the volume of network traffic has
considerably raised, and therefore, the current generation of mobile networks
cannot wholly meet the ever-increasing demands. Hence, it is envisioned that
the next generation, sixth generation (6G) networks, need to play a critical
role to alleviate such challenges in IoT by providing new communication
services, network capacity, and ultra-low latency communications (uRLLC). In
this paper, first, the need for 6G networks is discussed. Then, the potential
6G requirements and trends, as well as the latest research activities related
to 6G are introduced e.g., Tactile Internet and Terahertz (THz). Furthermore,
the key performance indicators, applications, new services, and the potential
key enabling technologies for 6G networks are presented. Finally, several
potential unresolved challenges for future 6G networks are presented.

###### Index Terms:

Internet of Things (IoT), 6G, 5G, uRLLC, THz, Tactile Internet, cellular IoT.

**footnotetext: Equal contribution in case of corresponding author

[Note: This work has been submitted to the IEEE Transactions on Network and
Service Management journal for possible publication. Copyright may be
transferred without notice, after which this version may no longer be
accessible ]

##  I Introduction

IoT is the most prevalent framework in the realm of Information and
Communication Technology (ICT). Cisco predicted that by 2023 there will be
14.7 billion devices connect to IoT [1] with various applications that are
generating a huge volume of data [2]. Moreover, the popularity of multimedia
services over wireless networks is exploding [1]. Therefore, supporting such a
volume of data demands new generations of cellular networks due to the
limitation of the previous generations.

Since 2019, the fifth-generation (5G) of cellular networks is commercially
available in several countries [3]. 5G uses revolutionary technologies, e.g.,
higher frequencies, network function virtualization (NFV), software defined
networking (SDN), and network slicing and evolutionary technologies, e.g.,
massive multiple-input and multiple-output (MIMO) to make a significant
improvement in data rates, energy efficiency, reliability, and connection
density. Meanwhile, 5G network is used in a wide range of applications such as
IoT, smart city, Industry 4.0 [4], e-health, wearables, smart utilities [5],
etc. Generally, the main 5G service classes include: 1) enhanced mobile
broadband (eMBB), 2) ultra-reliable low-latency communications (URLLC), and 3)
massive MTC (MTC). eMBB is capable of providing high data rates of 1G bits/s
for mobile users [6] while URLLC focuses on the reliability (99.999%) and
latency (milliseconds) of the communication, especially for the applications
such as industrial IoT (IoT) and vehicle to everything (V2X) [6]. mMTC
emphasizes on the number of connected devices in IoT employment (up to 1
million connections per km2) [7].

However, By taking the present-day and emerging advancements of wireless
communications into account, 5G may not meet the future demands for the
following reasons:

  * •

Given the ever-increasing growth of the deployment of IoT devices, there is a
particular need to improve further the connection density (10 million
connections per km2) and coverage of 5G-enabled IoT networks [8, 9].

  * •

New emerging services of IoT such as extended reality (XR), telemedicine
systems, mind-machine interface (MMI), and flying cars will challenge the
original 5G service classes. To effectively provide IoT services such as XR
and telemedicine systems for mobile devices, future mobile networks must
simultaneously provide high transmission rates, high reliability, and low
latency, which significantly exceeds the original goals of the 5G networks
[10, 11, 12].

  * •

It is expected that future mobile networks will be an ultra-large-scale,
highly dynamic, and incredibly complex system, e.g. the massive and
heterogeneous devices in the IoT. However, the architecture of the current
wireless networks (e.g., 4G and 5G) are often fixed, and the optimization
tasks are defined to cope with specific and identified challenges and
services. Hence, the prevailing manual-based optimization and configuration
tasks are no longer appropriate for future networks [13, 6, 14, 15].

To deal with the challenges mentioned above, 6G networks are expected to
provide new service classes, use new spectrum for wireless communications,
enormous network capacity, ultra-low latency communications, and adopt novel
energy-efficient transmission methods [16].

We aim to present a comprehensive survey on 6G cellular networks by
considering a wide ranges of 6G aspects. Our contributions can be summarized,
but not limited, as follows.

  * •

Discussing the need of a new generation of cellular networks beyond 5G.

  * •

Providing a detailed review on the existing works on 6G.

  * •

Introducing the 6G key performance indicators (KPIs) and new use-cases e.g.,
holographic communication and Industrial automation.

  * •

Studying various potential enabling technologies that will have important
contributions in 6G.

  * •

highlighting the potential 6G requirements, challenges, and trends e.g., Green
6G and 3D coverage.

  * •

Outlining several 6G future research directions.

The rest of this paper is organized as follows. Section II reviews the related
survey and magazine articles on 6G. In Section III, we present the
requirements and trends of 6G. The research activities and motivation are
discussed in Section IV. Moreover, in this section, we provide a comprehensive
list of 6G KPIs and use-cases, as well as new service classes. In Sections V
and VI, we introduce the important evolutionary and revolutionary technologies
respectively that contribute to the future 6G networks. We discuss several 6G
challenges in Section VII. Finally, Section VIII draws the conclusion.

Figure 1: The organizational structure of the survey.IIIIIIIVKPIVVIVIIVIII

## Glossary

3CLS

    Control, Localization, and Sensing
3D

    3-Dimensional
3GPP

    3rd generation partnership project
AES

    Advanced Encryption Standard
AI

    artificial intelligence
AID

    Autonomous intelligent driving
AMP

    approximate message passing
APs

    access points
AR

    augmented reality
AU

    aerial user
CAD

    cooperative automated driving
CRAN

    cloud radio access network
CSI

    channel state information
D2D

    Device-to-device
DL

    deep learning
E2E

    end-to-end
ECC

    Elliptic Curve Encryption
EI

    Edge Intelligence
EM

    emit electromagnetic
eMBB

    enhanced mobile broadband
ETSI

    European Telecommunications Standards Institute
GPS

    global positioning system
HMIMOS

    holographic MIMO surface
ICT

    Information and Communication Technology
IDS

    intrusion detection system
IIoT

    industrial Internet of things (IoT)
IoT

    Internet of things
IR

    infrared
ITU

    International Telecommunication Union
KPI

    key performance indicator
LIS

    Large Intelligent Surfaces
LoRaWAN

    Long Range Wide Area Network
LoS

    line-of-sight
MEC

    mobile edge computing
MIMO

    multiple-input and multiple-output
ML

    machine learning
MMI

    mind-machine interface
mMTC

    massive machine-type communication (MTC)
mmWave

    millimetre wave
MTC

    machine-type communication
NB-IoT

    Narrow band IoT
NFV

    network function virtualization
NMO

    Network Management and Orchestration
NOMA

    Non-orthogonal multiple access
NTC

    network traffic classification
NTMA

    network traffic monitoring and analysis
NTP

    network traffic prediction
OFDM

    Orthogonal frequency-division multiplexing
OMA

    orthogonal multiple access
OWC

    optical wireless communication
PLMN

    public land mobile network
QC

    quantum computing
QKD

    quantum key distribution
QOC

    quantum optical communication
QoE

    quality of experience
QoS

    quality of service
RA

    Random Access
RF

    radio frequency
RIS

    reconfigurable intelligent surfaces
RL

    reinforcement learning
SAL

    single-anchor localization
SDN

    software defined networking
SIC

    successive interference cancellation
SR

    super resolution
SVM

    support vector machine
SWAP

    size/weight and power
THz

    Terahertz
UAV

    unmanned aerial vehicle
UDHN

    ultra-dense heterogeneous networks
UE

    User Equipment
UHDD

    ultra-high data density
uHSLLC

    ultra-high speed-with-low-latency communication
uMUB

    ubiquitous mobile ultra-broadband
URLLC

    ultra-reliable low-latency communications
UV

    ultraviolet
V2X

    vehicle to everything
VLC

    visible light communication
VMR

    virtual meeting room
VR

    virtual reality
WIPT

    wireless information and power transfer
WPT

    wireless power transfer
XR

    extended reality

##  II Related Survey Articles

Several papers provide a vision of the 6G mobile networks including enabling
technologies, potential applications, requirements, and potential challenges.
In the sequel, we present a brief overview of the existing survey papers that
discussing different aspects of the 6G networks, and compare them with our
work.

The authors in [17] provided a vision of the 6G network and its requirements
(e.g., battery lifetime) from a user viewpoint. Meanwhile, the authors
envisioned some key enabling technologies for 6G, such as energy harvesting
techniques, the
glsai and
glsml. In [18] the authors investigated driver factors for 6G (i.e.,
holographic, massive connectivity, and time sensitive/time engineered
applications), the 6G network design principles, and propagation
characteristics of the 6G networks. The authors in [19] introduced the state-
of-the-art of
glslis, and then 6G.

The work in [20] discussed the evolution of mobile networks to the 6G network.
More specifically, the authors focused on expected architectural changes,
e.g.,
gls3d architecture and pervasive AI in the 6G networks. They highlight that
these changes are essential for cellular networks to satisfy future use cases’
demand. In [21], the authors paint a comprehensive picture of 6G, foreseen
applications, emerging trends, enabling technologies, and open research
challenges. The authors in [5] presented a vision of the 6G for wireless
communications and discuss the applications and critical abilities of 6G. They
also refer to
glsai as a key enabling technologies in the 6G era to achieve autonomous
network and producing innovative services/applications. As AI-based mobile
applications become increasingly popular, the authors in [22] discussed 6G
networks from an
glsai perspective. In this paper, the authors pointed out that the 6G networks
will be expected to adopt ubiquitous
glsai solutions from the network core to the edge devices. In [23], the
authors highlight the need for a new communication wireless network from
technological and societal perspectives. Then, they list several new use case
scenarios that are not possible to be efficiently supported by the current 5G
networks, such as holographic communications, smart environments, and high-
precision industrial manufacturing. A similar work towards envisioning 6G
wireless communications has been performed in [16]. They described the
potential 6G requirements and provided a sketch of the latest technological
achievements evolving to 6G networks. Furthermore, the authors presented
several technical challenges related to 6G and potential solutions.

Table I: Comparison of Related Survey Articles. Research |  Year |  Requirements |  Trends |  Vision |  Service Classes |  Architecture |  Applications |  Challenges |  Enabling Technologies
---|---|---|---|---|---|---|---|---|---
[17] |  2018 |  |  |  |  |  |  |  |  Optical communication, radio charging
[20] |  2019 |  |  |  |  |  |  |  |  Terahertz, visible light communication (VLC), energy harvesting, molecular communication, blockchain quantum computing (QC)-assisted, intelligent reflecting surface (IRS)
[21] |  2019 |  |  |  |  |  |  |  |  Terahertz, VLC, edge AI, non-terrestrial technologies, (IRS), energy harvesting
[5] |  2019 |  |  |  |  |  |  |  |  Blockchain, terahertz, LISs, molecular communication, QC-assisted, VLC and laser, OAM multiplexing, holographic beamforming
[22] |  2019 |  |  |  |  |  |  |  |  AI, big data, AI-powered closed-loop optimization, intelligent communication
[23] |  2019 |  |  |  |  |  |  |  |  Edge AI, self-optimization, 3D coverage, terahertz and VLC, distributed security
[16] |  2019 |  |  |  |  |  |  |  |  Terahertz and VLC, mmWave band, non-terrestrial technologies, multi-mode ultra-massive MIMO, OAM multiplexing, multi-domain index modulation, AI and big data, heterogeneous networks
[24] |  2020 |  |  |  |  |  |  |  |  Pervasive AI, radar-enabled communications, cell-free network, metamaterials, VLC and WPT, energy harvesting, OAM multiplexing
[25] |  2020 |  |  |  |  |  |  |  |  AI-empowered air interface, AI-empowered optimization, sub-networks, hyper-specialized slicing, new cognitive spectrum
[26] |  2020 |  |  |  |  |  |  |  |  Terahertz and VLC, synthetic materials, very large scale antenna, blockchain, AI
[27] |  2020 |  |  |  |  |  |  |  |  AI/DL and distributed processing, space-air-ground-sea integrated (SAGSI), non-terrestrial technologies, super massive MIMO, advanced multiple access techniques, rate splitting multiple access (RSMA), WPT and energy harvesting
[28] |  2020 |  |  |  |  |  |  |  |  AI, terahertz, operational/environmental intelligence
[18] |  2021 |  |  |  |  |  |  |  |  New frequency bands, new physical layer techniques, multiple antenna techniques, intelligent surfaces, multiple-access techniques, AI
[29] |  2021 |  |  |  |  |  |  |  |  AI, blockchain, digital twin, IE, communication-computing-control convergence
[19] |  2021 |  |  |  |  |  |  |  |  LIS technology
Our survey |  2021 |  |  |  |  |  |  |  |  Terahertz, AI, non-terrestrial technologies, optical wireless technology, 3D network architecture, energy harvesting, quantum communications, LISs, edge intelligence
(: The paper investigated the determined factor, : The paper partially covered
that factor, and : The papers did not consider that factor)

One of the latest studies on 6G networks has been conducted in [24]. The
authors provided their vision of 6G networks and discussed 6G requirements.
Moreover, they speculated on the 6G era applications and shed light on the
main expected challenges. In [25], the authors presented a list of the 6G
requirements derived by the potential future use cases (e.g.,
glsar and
glsvr for industry and biosensors). The authors in [26] provided a systematic
overview of 6G networks. Their overview covers enabling techniques, potential
use case scenarios, challenges, as well as prospects and development of 6G.
One of the few articles that well investigate the 6G network’s core services
has been conducted in work [27]. The authors in [28] focused on the future
mobile network, with the aim of giving a vision for 6G that would guide the
researcher in the beyond 5G era [30]. The authors refer to this critical fact
that 6G will still be a human-centric network. Regarding this fact, tight
security and privacy would be essential requirements of 6G networks.  Last but
not least, in [29], the authors conducted a survey covering potential use
cases, core services, vision, requirements, and enabling technologies of the
6G mobile systems.

To the best of our knowledge, most of the existing survey papers on 6G do not
fully cover all aspects of the 6G networks. We have listed the published
survey papers in Table I. The table summarizes the existing survey paper that
focuses on different aspects of the 6G networks such as requirements, trends,
vision, service classes, architecture, applications, challenges, and enabling
technologies. We represent the topics included in these survey papers and the
respective topics in our work. Compared to the existing literature, the
objective of this paper is to give a comprehensive view of 6G networks in
various aspects. To this end, we intend to respond to the following
fundamental questions:

  1. 1.

Is there any need for a new mobile network generation? If yes, why?

  2. 2.

What are the fundamental requirements and trends relevant to the 6G mobile
networks?

  3. 3.

What are the most recent research activities in the 6G network domain?

  4. 4.

What are the critical 6G KPIs and potential applications?

  5. 5.

What are the new service classes in the 6G era? And why will these services be
needed?

  6. 6.

Which are the most critical enabling technologies for 6G and need further
study?

  7. 7.

Which are the main expected future challenges and open issues in the 6G era
that call for significant efforts to resolve?

We use seven factors for evaluation of the existing works on 6G networks,
including requirements, trends, vision, service classes, architecture,
applications, and challenges. To the best of our knowledge, the existing
papers in the field of 6G networks only partially considered these factors
(See Table I). This work moves beyond the previously mentioned papers and
mainly focuses on the fundamental aspects of 6G networks (e.g., requirements,
trends, and KPIs). Unlike the existing works on 6G that does not fully cover
all essential aspects of 6G ( i.e., requirements, trends, visions, new
services classes, architectures, applications, challenges, and enabling
technologies) (see Figure 2), we try to cover all these aspects to an
acceptable extent.

Figure 2: 6G vision: Enabling technologies, and requirements and trends

##  III 6G Requirements And Trends

To tackle the current challenges of the current cellular networks are
involved, e.g. quality of service (QoS)-provisioning in terms data rate,
latency and quality of experience (QoE), the operators must consider new
strategies such as operation in shared spectrum bands, inter-operator spectrum
sharing, heterogeneous networks, leasing networking slices on-domain, etc. Ass
announced by the corresponding authorities, 6G will need more stringent
requirements in compare with 5G as shown in Figure 3. In particular, key 6G
requirements and trends are introduced as follows: [31].

  * •

Broad frequency bands: According to the requirements of the envisioned use-
cases of 6G, it is salient that the bands allocated to NR, e.g. sub-6 and
mmWave bands, will not be able to support the required QoS and QoE. Therefore,
it is predicted that future networks require higher frequency spectrum bands,
such as 73 GHz, 140GHz, 1THz, 3THz.

  * •

Opportunistic data-rate: To support the emerging application such as immersive
multimedia, a very high and opportunistic peak data rate is required [32].

  * •

Opportunistic latency: 6G requires the latency to be zero and end-to-end delay
to be less than 1 ms. For instance, XR services require a latency to be close
to zero in order to improve the QoS [33]. Furthermore, telepresence require
the latency must be lower than sub-millisecond [31].

  * •

mMTC: There will be a number of connected devices that need to be supported by
6G. The current trend, e.g. human-centric solutions will not be efficient due
to the network complexity and the sheer number of the connected devices.
Therefore, a new trend, machine-centric, will be necessary to support such
huge number of devices. However, there will be some critical challenges in
this context including scalability, efficient connectivity, coverage
improvement, as well as QoS and QoE-provisioning. Moreover, along with high
data rate and low-latency communications, 6G use-cases (e.g., mission critical
applications) also demand fast connectivity and high reliability/availability
(i.e., %99.999). Towards this end, the 6G networks must comply with strict
requirements such as availability, very short latency, and reliability, known
as super MTC (sMTC). sMTC will play a critical role in the real-time control
process in cyber-physical systems, vehicular communications, and Industry 4.0
operations.

  * •

Self-X network: In comparison with the previous generations, 6G networks must
be more flexible and robust. Such kind of flexible and robust networks are out
of human ability to control. Therefore, to manage such networks, sophisticated
machine learning (ML) techniques are of vital importance. ML techniques are
used to support the network autonomy as well as capture insights and
comprehension of surrounding environment in which they operate. Hence, with
the help of ML algorithms, 6G network will be self-learning, self-
reconfiguration, self-optimization, self-healing, self-organization, self-
aggregation, and self-protection.

  * •

Super-precision operating: It is clear to see that the traditional statistical
multiplexing techniques are not adequate to support the future high-precision
services and applications. Such as, tele-surgery and intelligent
transportation systems. Such services and applications require very high-level
and guaranteed precision, e.g., absolute delivery time. To do so, a bunch of
new function components are necessary to utilize such as user-network
interfaces, reservation signaling, new forwarding paradigm, intrinsic and
self-monitoring operation, administration, and management for network
configuration.

  * •

Super-precision positioning: global positioning system (GPS), uses signal and
travel time to find a position. However, the precision of such services are
not adequate according to the error that they struggle with, even in order of
meters [34]. The future services require very high precision in positioning
even in sub-millimeter accuracy. Tele-surgery and tactile Internet are of
those services.

  * •

Scalability: IoT is going to connect billion of devices ranging from high-end
devices to sensors, actuators, smartphones, tablets, wearable, home
appliances, vehicles, and many more to the Internet [35]. A huge amount of
data is generated via such connected devices. In order to extract the relevant
hidden knowledge, ML is required. ML-enabled devices can analyze the data and
extract the hidden knowledge and hence solve the issue of raw data
transmission, which results in improving the network resources utilization.

  * •

Supper energy efficiency: 6G devices are supposed to operate in higher
frequency band and therefore they need much more energy compared to 5G
devices. As an example, energy harvesting is under investigation to overcome
the issue of energy efficiency in 6G.

  * •

Connectivity in 3D coverage: 6G users will be able to experience beyond 2D in
6G applications such as 3D holographic display [36]. To achieve such wonderful
services, terrestrial and aerial devices will be employed.

  * •

Integration with satellites: Satellite communication technologies will be used
in 6G to provide global coverage. Telecommunication satellites, earth imaging
satellites, and navigation satellites will be integrated in 6G in order to
provide localization services, broadcast and Internet connectivity.

  * •

SDN: network management in 6G needs dynamic and programmatically efficient
network configuration. NFV enables the consolidation of network instruments
onto the servers located at data centers, distributed network devices, or even
at end-user premises. Moreover, network slicing offers a cognitive and dynamic
network framework on-demand, which can support several virtual networks on top
of shared physical infrastructure.

Figure 3: The requirements of 5G and 6G.

##  IV 6G Research Activities and Motivation

6G mobile network is envisioned to simultaneously meet future applications’
stringent requirements, such as ultra-high reliability, efficiency, capacity,
and low latency. Motivated by these foreseen advantages, several research
institutes and countries have started research projects on 6G network
developments. For example, the International Telecommunication Union (ITU)
organized a research group for the network 2030, namely FG NET-2030 [37]. The
central aim of FG NET-2030 is to recognize the suitable set of system
technologies to meet the future applications’ requirements, such as ultra-
massive connectivity and various types of resource requirements. In Finland,
the University of Oulu collaborates with NOKIA on the 6G network, _6Genesis_
[38]. The project started at the beginning of 2018 to conceptualize the 6G
enabling technologies. In 2017, the European Union (EU) developed a three-year
research plan aimed at identifying the essential system technologies of the 6G
network [39]. One of the funded projects is TERRANOVA, which aims to study and
confirm the usefulness of ultra high bandwidth wireless links working in the
THz frequency band. The United States, as one of the primary countries in
6G-related research, decided to use the terahertz frequency band for 6G.
Towards this end, Wireless Center at the New York University (NYU WIRELESS) is
designing THz communication channels with up to 100 Gbp/s data rates [40]. In
China, it launched research on 6G from March 2018 to meet IoT applications’
proliferation in the future [5]. LG Electronics company signed an agreement
with the Korea Advanced Institute of Science and Technology to develop 6G
wireless communication systems in South Korea [11]. The project’s main focus
would be on studying THz communications and wireless solutions to achieve 1
Tb/s data transmission speed. Samsung started the study on the 6G networks in
June 2019 and released its 6G vision in June 2020 [41]. This vision discussed
different aspects of 6G, including enabling technologies, new core services,
and requirements. 6G wireless summit is another research activity towards
enabling the next generation of cellular communications. Lapland, Finland, in
2019, has hosted the first 6G wireless summit [42]. Industry companies such as
Ericsson, Huawei, ZTE, and NOKIA are the summit’s principal patrons. The 6G
wireless summit’s main objective is to identify the critical 6G challenges,
potential use cases in the 6G era, possible technical requirements, and
candidate enabling technologies.

In the following section, we discuss the main 6G
glsplkpi and foreseen applications.

###  IV-A The Key 6G KPIs and Use-cases

This section discusses the main KPIs and characteristics of 6G applications
that are envisioned to be widely implemented in the future. A schematic
classification of the envisioned services and use-cases for 6G is represented
in Fig. 6.

  * •

Holographic communication: holographic telepresence will be one of the most
critical applications of 6G for both profession and social communication [43].
It will enable users to enrich their traditional audiovisual communication
with the sense of touch, while they are in different geographical locations.
Holographic communication is a highly data-intensive application, and 5G is
unexpected to deal with many holographic communications with absolute
reliability. This is mainly due to the fact that holographic imposes strict
requirements such as terabits data rate (up to 4 Tb/s), ultra-low latency
(sub-milliseconds), and reliable communications.

  * •

Industrial automation: It is foreseen that the Industry 4.0 transformation
will complete in the era of 6G, i.e., the digital transformation of
traditional manufacturing and industrial processes via cyber-physical and IoT
systems. The central goal of Industry 4.0 will be to decrease the demand for
direct human intervention in manufacturing practices through automatic control
systems and networks and communication systems. Toward this end, 6G has to
meet tight KPIs and requirements, such as a significant level of reliability
(i.e., above $1–10^{-9}$), very low latency (i.e., below 1 ms), and multiple
connected links [44]. Also, some industrial control operations (e.g.,
industrial augmented reality (AR) and virtual reality (VR)) call for
establishing real-time communications with very low delay jitter of $1\mu s$
and Gbp/s peak data rates.

Moreover, it is envisioned that the 6G network can provide exceptional help
for connected robotics and autonomous systems, such as unmanned aerial vehicle
(UAV) delivery services, the swarm of self-governing drones, air-ground
integrated vehicular network (AGVN) [45], and autonomous cars. 6G will enable
autonomous vehicles to engage more actively in everyday life, industry, and
transportation. More specifically, 6G will fully actualize the large-scale
deployment and services of autonomous cars [46].

  * •

Smart environments: The concepts of smart cities and smart homes refer to the
smart environments that can significantly increase the quality of life by
optimizing the services and operations, resources management, functions
automation, services efficiently, monitoring, etc. Accordingly, the 6G network
will combine ICT and an ultra-massive number of smart-physical devices (i.e.,
IoT device) to optimize daily life processes such as home security waste
management, transportation systems, traffic monitoring, and utilities-related
operations, to name a few. Realizing smart environments is one of the critical
goals of the 5G network; however, in the 5G era, these applications can
partially be realized due to their stringent requirements. One can refer to
the high connection density, ultra-high reliability communications (e.g., for
transportation systems), tight security (e.g., for smart homes applications),
and massive data rates (e.g., for social XR and connected cars).

  * •

E-Health: 6G will also offer enormous advantages to the health system through
technological innovations such as holographic communication, artificial
intelligence (AI), and AR/VR. 6G will help the health system by excluding time
and space boundaries through telemedicine and optimizing health system
functions / workflow [47]. Guaranteeing the eHealth services will require
satisfying demanding QoS requirements such as reliable communication
(99.99999%), ultra-low latency ($<$ 1 ms), and mobility robustness.

  * •

Tactile Internet: The 5G-enabled IoT systems mostly focus on perception and
connectivity. In the future, 6G will provide a more intelligent human-to-
machine type of communication for real-time controlling IoT devices, namely
tactile Internet. According to [26], the tactile Internet is a wireless
communication system that will enable humans and machines to exchange control,
touch, and sense data in a real-time manner. The tactile Internet will enable
technology for haptics interface and, consequently, possible visual feedback
and remote response behavior. The tactile Internet is expected to play a vital
role in different services, such as Industry 4.0, everyday life, cooperative
automated driving (CAD), e-commerce 4.0, and other human-machine interaction-
based applications. To enable tactile communications in the future, 6G has to
provide ultra-real-time response, ultra-low latency and reliable
communications. One typical application for tactile Internet is presented in
Figure 4.

Figure 4: Remote surgery Figure 5: Relative requirement scores for the seven
applications – representing graph by ball diameter. Adopted from [48].

  * •

Private 6G networks: Different industries and corporations (e.g., factories,
airports, oil and gas, health sector, and grids) have employed IIoT to create
an autonomous network for control and monitoring tasks without human
intervention. To realize IIoT, the establishment of the underlying network is
crucially important. Traditionally, the underlying network has been created by
using both wired (e.g., fiber, industrial Ethernet, and Fieldbus) and wireless
technologies (e.g., WiFi, WiMAX, and Bluetooth). However, these communication
technologies still fail to satisfy the stringent requirements of IIoT
applications, such as security, low latency, and reliability. Moreover, the
ever-increasing number of IIoT devices, their mobility, and the rise of
security and privacy threats motivate the industrial community to replace
these technologies with private cellular networks [49]. New launched 5G
cellular networks provide an effective alternative to the traditional IIoT
underlying network, named the private 5G network, to fulfill stringent
communication requirements in industry and other sectors. The private 5G
network refers to a 5G new radio technology-based local area network for
dedicated wireless coverage in a particular area. The private 5G network can
provide distinctive features, such as mobility, positioning, improved
security, guaranteed QoS, exclusive capacity, customized service, and
intrinsic control that are especially attractive for industrial communication.
Another expected evolution path will be the transition from private 5G
networks to private 6G networks. This is mainly because many more industries
may participate in the competition of adopting custom solutions tailored for
their actual use cases, such as industrial automation, warehouse operations,
and remote industrial operation.

  * •

AR and VR: AR and VR have considered being one of the most distinguished
services of the 5G networks. 5G has been adopted the new frequency spectrum
(i.e.,millimetre wave (mmWave)) to increase the network capacity, and
consequently support data-hungry applications such as AR and VR. AR/VR
technologies dramatically affect many research areas and provide new use
cases, such as remote surgery, MMI, haptic technology, and game technology
[50]. These use cases will need a different level of latency and reliability.
Despite this fact that some of the 5G service classes (e.g., URLLC) provide
high reliability and low latency communications, some extremely-sensitive use
cases (e.g., remote surgery ) will need latency to be shorter than one
millisecond, which is not still feasible in the forthcoming 5G network.
Moreover, other up-coming VR-based applications such as haptic technology and
virtual meeting room (VMR) call for massive transmission amounts of real-time
data expected to exceed the capability of 5G. Hence, they will need the 6G
system to satisfy the end-to-end latency requirements. Figure 5 represents the
relative significance of each requirement for the applications.

Figure 6: The envisioned services and use-cases for 6G [31].

###  IV-B New Service Classes for 6G

The applications mentioned above and related requirements will lead to new 6G
service classes. These service classes expected to refine 5G core service
classes, i.e., URLLC, eMBB, and mMTC. In the following, we explain the
identified new service classes for the 6G network.

  * •

Massive URLLC:5G URLLC refers to communications with high reliability, low
latency, and high availability for mission-critical scenarios, such as IIoT
and remote surgery. URLLC for many devices will be an essential scenario for
future communication systems and networks [51]. Towards this end, 6G has to
increase the size of 5G URLLC service to a massive scale, leading to a new
service class, called massive URLLC, which combines 5G URLLC with classical
mMTC. Autonomous intelligent driving (AID) is one of the foreseeable
applications of massive URLLC, in which several important considerations must
be taken into account simultaneously, such as motion planning, automated
driving, automatic vehicle monitoring, obstacle detection, emergency rescue
operations, and so on. Massive URLLC 6G must provide low latency, high
reliability, high data rate, massive connectivity, and full mobility at the
same time to meet such applications’ requirements. To realized massive URLLC,
multiple access techniques such as OMA, NOMA, and contention-based multiple
access could be promising solutions. By applying OMA techniques, massive URLLC
6G could experience a linear increase in the required bandwidth along with the
rise in the number of devices. Moreover, other multiple access techniques
(e.g., NOMA and contention-based) can be used to achieve proper trade-offs
among latency, reliability, and scalability [52]. Massive URLLC calls for the
transmission of massive short-data packets to guarantee time-sensitive 6G
applications with excellent resource efficiency and low latency.

  * •

eMBB: Furthermore, some other application such as AR, VR, and holographic
meetings are promising examples of 5G and beyond 5G applications. Such
applications often need high transmission rates (e.g., high-quality video
streams), low latency (e.g., real-time interactive instructions), and high-
reliability communications [24]. Moreover, these requirements must also be
satisfied in high mobility conditions such as sea and air travel. As a result,
a new service class, the so-called enhanced mobile broadband URLLC (eMBB \+
URLLC), has been envisioned to allow 6G to support any scenarios subject to
the requirements of the high rate rate-reliability-latency. Energy efficiency
will be a severe concern for this service class due to its direct effect on
reliability and data rate. Compared with the eMBB and URLLC in the 5G
networks, this envisioned service class should be highly competent in
optimizing mobile communication systems in terms of the handover process,
interference, and big data transmission/processing. Furthermore, the security
threats and privacy concerns related to enhanced mobile broadband URLLC
communication service shall be considered. It seems that the developing
resource sharing technique for the coexistence of uRLLC and eMBB services in
the 6G networks is one of the most significant technical challenges towards
enabling enhanced mobile broadband URLLC [53].

  * •

Massive eMBB: in Section IV-A, we mentioned that tactile Internet would be one
of the 6G use cases (e.g., Industry 4.0), which will pose stringent
requirements such as high data rates, ultra-low latency, and reliable
communications [5]. Meanwhile, to gain tactile perceptions and convert them
into digital information, the connection density would often be very high in
Industry 4.0-based scenarios(e.g., 100 connections in $m^{3}$). Hence, massive
eMBB will attract lots of interest in the 6G era for improving the operations
and functions in large-scale IIoT by supporting massive low-latency
connectivity among workers, sensors, and actuators.

Beyond these services, the works have been conducted in [22] and [54]
envisioned three other service classes for 6G, including Computation Oriented
Communications, Event Defined uRLLC, and Contextually Agile eMBB
Communications. In [55], Zong et al. refer to the fact that advances in
industry and autonomous intelligent driving will lead to the appearance new
core service classes in the future network, such as ultra-high speed-with-low-
latency communication (uHSLLC), ultra-high data density (UHDD), and ubiquitous
mobile ultra-broadband (uMUB). Considering foreseen 6G usage scenarios and
KPIs, a different classification of new service classes will be supported by
6G is introduced in [5]. These services include extremely reliable and low-
latency communications, ultra-massive machine-type communications, further
enhanced mobile broadband, ultra-low-power transmissions, and long-distance
and high-mobility communications. Moreover, Human-Centric Services and Multi-
Purpose Control, Localization, and Sensing (3CLS) and Energy Services have
been introduced as new 6G service classes [21].

In the next section, we discuss the technologies that are expected to be
integrated into 6G as enabling technologies.

##  V Evolutionary Technologies of 6G

This section and the next one investigate the technologies arising as enablers
of the 6G network use-cases and related KPIs, discussed in Sections IV-B and
IV-A. Some of these technologies have already been considered or discussed in
the 5G networks; However, due to the technological limitations or market
boundaries, they are not commercially available for the 5G networks. The 6G
breakthroughs can happen in different layers (e.g., PHY), network
architecture, communication protocols, network intelligence, etc. This section
is dedicated to evolutionary technologies of 6G. As mentioned above,
evolutionary solutions aim to use existing or previously adopted technologies
(e.g., MIMO) to realize the 6G networks, where revolutionary solutions aim to
exploit novel technologies (e.g., THz communications) to serve 6G. In other
words, the revolutionary technologies will fundamentally change different
layers of cellular networks (e.g., PHY layer) compared to the 5G networks.
Note that many aspects of the revolutionary technologies are still at the step
of scientific investigation.

###  V-A Non-Terrestrial Technologies

The existing cellular networks based on legacy terrestrial technologies have
risen to the challenges of providing extensive wireless coverage to rural
areas, the lack of availability/ reliability, and vulnerability to natural and
human-made disasters. To address these challenges, the 6G networks will
integrate with non-terrestrial technologies, i.e., UAV-assisted wireless
communications and satellite connectivity, in order to afford full coverage
and high capacity connectivity [56].

  * •

UAV-assisted wireless communications: UAVs can fly at low altitudes (>100 m)
and have recently obtained ever-increasing interest thanks to their simplicity
and low cost to implement broadband broad-scale wireless coverage during
emergencies or act as relay nodes for terrestrial wireless communications. One
of the promising foreseen applications of UAV-assisted communications will be
in 6G-enabled IoT networks. UAVs can overcome geographical and environmental
limitations on wireless communications such as ships on the ocean, deployed
sensors in the remote/ isolated regions, and out of terrestrial network
coverage areas. It is almost impossible for traditional IoT communication
technologies such as Long Range Wide Area Network (LoRaWAN) and Narrow band
IoT (IoT) to be applied to such situations.

Regarding integrating UAVs into the existing and future cellular networks, two
possible scenarios can be imagined [57]. In the first scenario, UAVs can be
incorporated into the cellular network as a new category of User Equipment
(UE) that gets services while it flies in the sky, referred to as aerial user
(AU). It is envisioned that AUs will be a win-win solution for UAV
technologies and cellular networks because it is a cost-effective solution.
More significantly, AUs can reuse many installed cellular base station (BS)
without the need to construct new necessary infrastructures. AUs will
introduce many new UAV-based use cases in the 6G era, such as urban/ road
traffic control, search and rescue missions in remote areas, and environmental
photography. Moreover, AUs can be used as a complementary tool to help the
positioning systems based on cellular networks. In the second scenario, UAVs
can be work as aerial BS or relay nodes to help legacy terrestrial wireless
communications by connectivity from the sky. Non-terrestrial BS/relay nodes
could deliver enormous benefits compared to terrestrial BS/relays installed at
fixed sites [58]. For instance, aerial BS/relays can be quickly established if
desired. This feature is crucially essential for use case scenarios, such as
emergencies and unforeseen disasters, and search and rescue purposes.
Moreover, compared to the terrestrial BSs/relays, aerial BSs/relays are more
likely to establish radio link with terrestrial UEs since they are above the
ground. Hence, they can make more reliable connections and provide multi-user
scheduling and resource allocation for radio access.

Several significant issues, however, posed by UAVs-based communications, such
as the different communication QoS requirements for UAV command/control
signals and payload data, severe interference in air-ground communications
(uplink/downlink) due to the line-of-sight (LoS)-dominant channels, and
challenges related to UAVs size/weight and power (SWAP) constraints.

  * •

Satellite communications To achieve ubiquitous connectivity, 6G will integrate
satellite communications with the cellular network. High throughput satellite
technology will be widely used for establishing broadband Internet
connectivity, especially for the remote and out-of-coverage areas of
terrestrial communication networks. It is expected that this Internet access
service to be competitive with ground-based services in terms of pricing and
bandwidth. In the 6G era, satellite-enabled communication is a anticipate
alternative for terrestrial and UAV-assisted communication technologies to
provide global connectivity for the smart-physical devices on the ground and
hence can be utilized for IoT scenarios. Recently, some efforts have been
initialized by the 3rd generation partnership project (3GPP) to provide
satellite communication standards in order to serve upcoming terrestrial
wireless communications [59]. Motivated by the enormous advantages of
satellite-assisted IoT communications such as communication reliability, broad
coverage, security/protection, fast rural deployment, and long longevity [60],
some satellite communications companies, e.g., Globalstar and Iridium
Communications, are involved in designing dedicated satellites for satellite-
based IoT networks. One can refer to several applications for satellite-based
IoT networks, including mission-critical services, location-based services,
navigation systems, healthcare sector, to name a few.

###  V-B AI and 6G

Maybe the most influential and recently proposed enabling technology for the
6G network is AI [31, 61]. AI has been used in 5G somewhat or very limited,
especially ML algorithms such as deep learning (DL) and reinforcement learning
(RL). Classical ML algorithms also have been applied to 5G, for instance,
support vector machine (SVM) and bayesian networks. One can refer to a wide
range of ML applications in the 5G era, such as network traffic classification
(NTC)/network traffic prediction (NTP), intrusion detection system (IDS), and
network traffic monitoring and analysis (NTMA) to name a few [62].
Nonetheless, 6G will fully operationalize AI for different purposes (e.g.,
intelligent reasoning, decision making, and design and optimization of
architecture, operations, and protocols), beyond the classification/prediction
tasks. The 6G network is expected to provide ubiquitous AI services from the
core to its end devices.

AI can leverage massive training data to learn, provide forecasts, and make
decisions, making it a powerful tool for enhancing wireless networks’
performance, even when comprehensive system information is not available.
Hence, AI can be considered a foundation stone for different aspects, e.g.,
design and optimization, of future wireless networks, especially the 6G
network. It is predicted that the significant impacts of AI on 6G will be in
air interface design and optimization. AI methods have been first broadly
adopted to meet challenges in the upper layers of communication systems and
networks, and achieve remarkable successes [62]. Motivated by these successes,
and stimulated by the foreseen KPIs (see Section IV-A) and challenges of the
next-generation mobile network (see Section VII), AI-based approaches have now
been studied also at the other layers of communication systems and networks.
In the following, we discuss AI-enabled methodologies and technologies for the
6G network.

SDN and NFV, also known as network softwarization, are crucial 5G enabling
technologies that enable architectural innovation, such as network slicing.
Network slicing is one of the key innovative design aspects in the 5G network
that allows enormous virtualization capability and, consequently, encourages
diverse enterprise business models with a considerable degree of flexibility,
more profits for the service provider, and lessen operational costs. However,
6G will be a more complex and heterogeneous network, and hence, network
softwarization will not be sufficient. As mentioned, 6G will benefit from new
radio interfaces access types such as the THz frequency band and intelligent
surfaces. Moreover, the 6G network will need to serve more difficult IoT
related functionalities, e.g., sensing, data gathering, data analytics, and
storage. When put all together, 6G will need an adaptive, flexible, and
intelligent architecture. This calls for further improvement in the current
technologies (e.g., SDN, NFV, and network slicing) to meet the requirements
mentioned above. AI-based approaches will present a more versatile network
slicing architecture in the 6G era by allowing rapid learning and adaptation
to dynamics.

The ever-increasing growth in the volume and variety of data produced in
communication networks calls for developing data-driven network operations and
planning to adapt to future networks’ highly dynamic nature. Leveraging ML-
based methods for big data analytics is one of the AI applications that can be
used for the 6G network. The key applications of AI in 6G networks include
[63]:

  * •

Descriptive analytics: Descriptive techniques refer to presenting historical
data to easily understand different aspects of the network, such as channel
conditions, network performance, traffic profiles, etc.

  * •

Diagnostic analytics: Using AI, 6G networks can use the network data to detect
the network faults, find out the faulty services, identify the root causes of
the network anomalies, and thus enhancing the network performance in terms of
reliability and security [64, 65].

  * •

Predictive analytics: Using AI, 6G networks can provide forecasts about the
future or unknown events, such as traffic patterns and network congestion,
resource availability, and future locations of the users.

  * •

Prescriptive analytics: Prescriptive techniques leverage descriptive and
predictive analytics to help with the decision about network slicing (e.g.,
number of slices), virtualization, caching placement problems, and resource
allocation.

As mentioned earlier, 6G will be a highly dynamic and complex network because
of the extremely large-scale, high connection density and heterogeneity.
Hence, conventional approaches for wireless network optimization (e.g.,
mathematical and dynamic programming) will not be applicable for the 6G
network [22]. As a result of this incapability, it is expected that 6G will
benefit from AI-enabled automated and closed-loop optimization. Recent
advances in ML methods (e.g., deep RL) can build a feedback loop system
between the decision-making agent and the cyber-physical systems [66]. The
agent can iteratively improve its performance by receiving the system’s
feedback to achieve optimal performance eventually [67].

###  V-C Energy Harvesting

Energy harvesting mechanisms have been incorporated into 5G to meet strict
energy limitations affordably and sustainably. These mechanisms can generate
electrical power from the external sources for the energy supply of network
devices, e.g., BSs and UEs. However, 5G energy harvesting mechanisms currently
encounter some challenges, such as the coexistence of these mechanisms with
communication protocols and efficiency degradation during converting harvested
signals to electricity. Considering the foreseen massive scale of the 6G
network, and stimulated by the fact that any sustainable development in
communication systems and networks should devote careful attention to energy
consumption, 6G will have to develop effective energy harvesting mechanisms
and energy-efficient communication techniques. Moreover, it is expected that
enabling IoT in the 6G era through the massive connectivity of low-power and
batteryless smart devices. Nonetheless, finding a practical solution (s) to
increase batteryless devices’ lifetime is a serious issue. Two potential
solutions have attracted increasing attention for tackling this issue,
including 1) further improve the energy efficiency of low-power devices, and
2) energy harvesting mechanisms and wireless information and power transfer
(WIPT) [68]. Emerging 6G enabling technologies such as Terahertz (THz)
communications and intelligent surfaces open up ample opportunities to achieve
the energy self-sufficiency and self-sustainability vision for the 6G network.
For example, because of its better directionality, the THz frequency band is
more efficient than lower frequency bands for WIPT scenarios.

###  V-D Large Intelligent Surfaces (LIS)

As we mentioned in Section IV, the spectral efficiency also is one of the 6G
KPIs. Massive MIMO is a leading-edge technology to improve the spectral
efficiency in which it is possible to serve many UEs in a cellular cell over
the identical bandwidth. Besides, the THz frequency band is one of the
technologies that promise to help the spectral efficiency. However, in the 6G
era, the simultaneous deployment of traditional Massive MIMO technology and
THz frequency band can cause several challenges, including high power
consumption, extreme complexity in signal-processing algorithms, and increased
hardware cost equipment. Using LISs for communications will be a solution to
alleviate these challenges in the 6G network. Intelligent surfaces are smart
electromagnetic materials that can be embedded in our environments, such as
buildings, walls, and clothes. These surfaces able to change the reflection of
radio waves and expected to lead to the introduction of new communication
technologies such as holographic MIMO and holographic radio frequency (RF)
[69]. The concepts such as smart radio environments, reconfigurable
intelligent surfacess (RISs), and LISs are intelligent surfaces technology
branches, and each of them will bring its advantages. For example, RIS-
assisted systems can improve the UEs’ achievable data rate and increase MIMO
communication channel rank efficiency.

Among the technologies mentioned earlier, LISs gain more attention from
academia and industry. LIS refers to utilizing artificial electromagnetic
metasurfaces as large antennas in order to increase the capacity of the
network [70] or to adopt single-anchor localization (SAL) approach. Indeed,
LISs can be considered an extension of the traditional massive MIMO
technology, but with different array architectures and operating mechanisms.
Under using LIS technology, massive MIMO systems’ impressive performance gains
can be reached and improve these systems’ energy efficiency as LIS’ elements
do not dependent on any active power source to transmit data [71].

###  V-E mobile edge computing (MEC)

MEC, formerly recognized as mobile edge computing, is a network paradigm
defined by European Telecommunications Standards Institute (ETSI) and refers
to the deployment and execution of distributed computing capabilities, content
caching, and network data analytics and network decisions making at the
network edge [72]. MEC will become a primary player in the 6G networks as it
can act as an intermediate layer that allows active data analytics, where the
data is generated. This paradigm is crucially essential for resource-
constrained services/applications [73]. One can refer to V2X communication,
improving the energy efficiency and computing offloading of URLLC, and
security and privacy purposes as the prominent use cases related to MEC. There
are several emerging services, such as AR/VR and V2X, that need low end-to-end
(E2E) latency. In this case, MEC can dramatically decline E2E latency by
providing edge-based data processing and analytics approaches. Besides, MEC’s
localized data pre-processing capabilities can reduce the need for sending a
considerable amount of redundant or unnecessary data to the cloud data
centers. MEC is also expected to be used to efficiently manage network
resources (e.g., computational and communication). More specifically, the
deployment and operationalization of edge servers, also called MEC servers, at
the edge of the network can realize the semi-centralized resource allocation
schemes, in which centralized resource allocation techniques can be used to
assign the network resources to a cluster of edge devices in the presence of a
limited channel state information (CSI) and with low complexity.

###  V-F Non-orthogonal multiple access (NOMA)

Multiple access techniques have always been essential in developing
communication systems and networks, and 6G is not an exception [74]. NOMA will
become one of the most influential radio access mechanisms for the 5G and 6G
cellular networks. NOMA plays an essential role in the implementation and
optimization of polar coding and channel polarization methods. As a multiple
access technology, NOMA has been proposed for spectral efficiency in 5G/B5G.
In comparison to the traditional orthogonal multiple access (OMA)
technologies, NOMA also represent considerable improvement in terms of
security, secrecy capacity, and user fairness [75]. NOMA leverages different
techniques to provide these improvements, such as successive interference
cancellation (SIC) technique and strong/weak users’ decoding order. Massive
URLLC is envisioned as one of the leading service classes in the 6G networks,
where NOMA technologies have remarkable abilities in guaranteeing services
such as mMTC and URLLC. Adopting NOMA techniques is crucially vital for
current and future mobile networks because they can help improved bandwidth
utilization and efficient allocation of resources. Moreover, the MEC
convergence with the NOMA, also called NOMA-assisted MEC, can be further
studied to enhance the computation service in B5G [76].

###  V-G Device-to-device (D2D) Communication

It is expected that D2D communication will be one of the most innovative
technologies that help to fulfill the requirements of different emerging use
cases in the 6G era [14]. Towards this end, D2D communication can provide 6G
network infrastructure for various D2D-based solutions for NOMA, network
slicing, and MEC, to name a few. Furthermore, it is envisioned that low
latency and high-speed D2D communication will be essential for the 6G networks
to deal with the limited distance communication because of THz technology in
the future ultra-dense heterogeneous networks (UDHN).

Regarding MEC, the UEs’ spare resources (e.g., computational and
communication) can be used to improve network edge computing performance. D2D
can enable the 6G to use the idle resources by establishing a virtual network
infrastructure to manage the resources. Different topology management
techniques can be used by D2D, e.g., clustering, that can allow the network to
use spare resources efficiently [77]. In the timeframe of 6G, thanks to THz
band D2D technology’s employment, the communication between two nearby UEs
will be near real-time [78]. Hence, it is expected that the 6G networks will
use the capabilities of D2D clusters in edge computing.

In terms of network slicing, it is envisioned that the D2D-enhanced
intelligent network slicing mechanism will encourage telecommunications
operators to effectively centralize and combine network resources, such as D2D
clusters, private third party, and public land mobile network (PLMN) at the
edge of the network. Finally, NOMA’s utilization as a multiple-access approach
for D2D can enable a D2D transmitter to communicate with multiple D2D
receivers through NOMA. As a result, the performance gains of D2D will
significantly improve [79].

###  V-H Grant-free Transmission

Grant-free transmission technology has been introduced as one of the primary
trends in future mobile networks [80]. Indeed, this technology has been
classified as a critical medium access control mechanism for enabling massive
IoT connectivity over mobile networks. For the 5G networks, different grant-
free transmission techniques have been adopted for mMTC and URLLC services;
however, these techniques’ provided capacity is still restricted [81].
Regarding the ever-increasing growth in the number of smart-physical devices
and the popularity of these two services, more efficient grant-free
transmission technologies will need to be designed for the 6G networks.
Fortunately, the integration of NOMA with the grant-free transmission, GF-
NOMA, is a promising solution for the 6G-enabled IoT systems because of NOMA’s
short delay performance [82]. The majority of conventional NOMA techniques
considered a centralized scheduling scheme, in which IoT devices are already
connected, and different network parameters, e.g., spreading sequences and
power control, are pre-determined. However, due to the specific
characteristics of mMTC traffic, such as mass uplink communication, small size
and periodic data transmission, and various QoS requirements, the conventional
NOMA techniques’ performance can be highly degraded. In other words, this type
of traffic can cause signaling overhead and increase the latency of the
centralized scheduler. To deal with this challenge, the grant-free
transmission is a viable solution, where the devices can send their data
during randomly chosen time-frequency resources in an automatic manner to
realize low-latency access and decline signaling overhead associating with the
scheduling request. One can refer to signature-based, compressive sensing-
based, and compute-and-forward based as the main categories of grant-free NOMA
schemes [82].

###  V-I Sparse signal processing

Sparse sampling, also known as compressive sensing, is a sparse signal
processing paradigm that optimally utilizes sparsity in signals for
reconstructing them efficiently and with fewer samples. This paradigm’s
applications have been studied in different aspects of the 5G/B5G networks,
including MIMO random access, embedded security, cloud radio access network
(CRAN), channel-source network coding based on the compressive paradigm, etc
[83]. Sparse signal processing algorithms can be used to accurately and
effectively recognize active IoT devices in the grant-free transmission
approach. One of the main challenges in grant-free transmission, consequently
in enabling massive IoT connectivity, is to identify the active IoT devices
for data decoding [84]. Sparse signal processing is also important for
realizing THz communications in the 6G networks. Due to THz channels’ sparse
nature, compressive sensing methods for sparse channel recovery in THz
channels estimation can be used. For example, the work in [85] the
applicability of approximate message passing (AMP) as a compressive sensing
technique has been investigated in THz channel estimation. By leveraging the
sparsity feature, the compressive sensing paradigm demonstrates an excellent
ability to improve the spectrum and energy efficiency for the future wireless
networks and IoT systems.

###  V-J Holographic MIMO Surfaces

One of the key 6G enabling technologies is holographic MIMO surface (HMIMOS)
[86]. In the 5G networks, massive MIMO systems (i.e., BSs with large antenna
arrays) have been used to satisfy 5G networks’ throughput requirements.
Nevertheless, because of various reasons such as energy consumption concerns
and the considerable cost of fabrication/operation, it is difficult to fully
realize massive MIMO systems. Given the remarkable advances in programmable
metamaterials, RISs show enormous potential to deal with massive MIMO
challenges and develop the challenging vision for the 6G networks by
actualizing seamless connectivity and control of the environment in cellular
wireless networks through intelligent software. HMIMOS is expected to improve
massive MIMO technology in terms of size, cost, weight, and energy consumption
by transforming the wireless network environment into a reconfigurable
intelligent entity. Towards this end, HMIMOS may take three different roles,
including receiver, transmitter, and reflector. The distinguishing
characteristics of HMIMOS (i.e., intelligence and reconfigurability) make it a
prospective technology to fulfill the different 6G requirements, including
low-latency, low-power, and high-throughput communications. In terms of
communications, one can refer to two main groups of applications for HMIMOS,
including outdoor and indoor applications. HMIMOS outdoor applications include
energy-efficient beamforming, creating connections between the users and BS,
PHY layer security, and wireless power transfer (WPT), where indoor
applications are accurate indoor positioning and coverage enhancement in
indoor environments [86].

##  VI Revolutionary Technologies of 6G

As mentioned in Section V, 6G enabling technologies can be categorized to
evolutionary and revolutionary technologies. In this section, we study the
revolutionary technologies of 6G as follows.

###  VI-A THz Communications

Despite the successful 5G deployment with the help from enabling technologies
such as the mmWave frequency spectrum, the demand for enhancing data rates
continues. In this sense, higher frequencies above the terahertz band will be
fundamental in the 6G network. The terahertz frequency band is between the
mmWave and optical bands, and it ranges from 100 GHz to 10 THz [87]. THz
frequency band is promised to provide data rates on hundreds of Gbp/s(e.g.,
100 Gbp/s), secure transmissions, extensive connectivity, highly dense
network, and enhance spectral efficiency, consequently increase the bandwidth
(>50 GHz) to meet the requirements of 6G use cases with massive data rates and
ultra-low latency. Moreover, the THz frequency band benefits from high-
resolution time-domain, which is crucially vital for super resolution (SR)
sensing technology (e.g., remote sensing) and high precision positioning
services (e.g., autonomous driving). Despite these remarkable advantages,
multiple unique issues arising from the THz frequency communications. For
example, THz links are prone to excessive signal attenuation, rapid channel
fluctuation, and severe propagation loss, notably restricting communications
over long distances. Besides, to use the terahertz frequency band in
commercial communication systems, one should consider engineering-related
challenges, e.g., design very large-scale antenna and requiring high
computational power for supporting the extensive bandwidth.

Fortunately, rapid advances in infrastructure and algorithmic aspects of
communication systems, such as ultra-massive MIMO (UM-MIMO), intelligent
surfaces, new signal processing methods, and communication protocols, will
mature THz communications.

###  VI-B Optical Wireless Technology

Alongside mobile communications based on RF, optical wireless communications
(OWCs) will be widely used in the timeframe of 6G. OWC frequency range
comprises infrared (IR), VLC, and ultraviolet (UV) spectrum [88]. OWC is now
being operated since the 4G network. Nevertheless, it will be deployed more
broadly to satisfy the requirements of 6G. Among OWC technologies, VLC is the
most promising frequency spectrum because of the technology advancement and
extensive using of light-emitting diodes (LEDs). The OWC in the visible
spectrum (380 to 740 nanometers) is generally known as VLC, which visible to
the human eye.

For short-range communication distances (up to a few meters), VLC technology
offers unique advantages over its RF-based counterparts [89]. First, the
occupied spectrum by VLC systems is free and unlicensed, and they can provide
extensive bandwidth (THz-level bandwidth). Second, VLC-based communications do
not emit electromagnetic (EM) radiation and are not seriously interfere with
other potential EM interference sources. This means that VLC communications
can be adapted for sensitive EM interference applications such as gas
stations, aircrafts, and hospitals. Communication security and privacy is the
third advantage of VLC. The transmission medium in a VLC-based network cannot
penetrate walls and other opaque obstructions. In other words, the
transmission range of the network is limited to indoors. As a result, it can
protect users privacy and sensitive information from malicious adversaries.
This could be more interesting when we know that about 80% or more of the
time, people tend to stay indoors. Last but not least, VLC can rapidly
establish wireless networks and does not need expensive BS since it uses
illumination light sources as BSs.

The maximum data rate of OWC is highly dependent on lighting technology. For
example, in [90] [91] the authors claimed they achieve up to 4Gbp/s data rate
with a gallium nitride (GaN)-based LED. Given the technological improvements
of LED lamps and related fields, e.g., digital modulation techniques, it is
expected that the achievable data rate of VLC will reach hundreds of Gbp/s for
the 6G network [92]. It is envisioned that VLC technology will be widely used
in different applications, such as intelligent transportation systems (ITS),
smart cities and homes, the advertising and entertainment industry, and
hospitals.

###  VI-C 3-Dimensional (3D) Network Architecture

The currently and previously deployed cellular network architectures are
designed for 2-Dimensional (2D) connectivity between network access points and
ground-based UEs. Conversely, it is envisioned that the 6G network will
integrate the terrestrial and non-terrestrial technologies (see Section V-A)
to support 3D network coverage. Compared with the fixed 2D infrastructures,
the 3D strategy is much more timely and economically efficient
(telecommunications operators have to bear the cost of the deployment of dense
mobile networks to guarantee massive connectivity), especially when the
operators want to quickly provide seamless/reliable/continuous services in
rural areas or the case of natural disasters. 3D coverage will also enable
communication system for deep-sea and high-altitude. Despite the significant
advantages mentioned above, 3D network architecture will pose many challenges
that need to be responded before this technology can effectively be used in
real-world cellular networks, e.g., channel models for air-to-ground
communications, trajectory optimization, resource management, topology, etc.
[93] [94].

###  VI-D Edge Intelligence (EI)

EI or edge AI is another promising computing paradigm that gains enormous
interest [95] [96]. Big data sources as an enabling technology for learning-
based solutions have recently represented a significant shift from the cloud
data centers to the ever-increasing edge devices, e.g., smartphones and
industrial IoT devices [97]. It is evident that these edge devices can fuel AI
and present several new use cases by providing a massive volume of data.
Motivated by the marked shift in big data sources and stimulated by the
advantages mentioned earlier, there is an imperative action to push the AI
solutions to the edge of the network to exploit the edge big data sources’
potential entirely. Nevertheless, providing AI solutions at the network edge
is not a trivial task because of the issues related to performance, data/user
privacy, and cost. The traditional approach is to transfer the data generated
by the edge devices to the cloud data centers for processing and analytics to
deal with these issues. Clearly, transferring such a considerable amount of
data will bring monetary/communication costs and cause delays. Moreover, data
protection and privacy-preserving can also be significant concerns in this
scenario. On-device data analytics is proposed as a remedy, in which AI
solutions can be run on the edge devices to process generated data locally.
Nonetheless, in this alternative, lower performance and energy efficiency are
expressed as primary concerns [98]. This is mainly because most AI solutions
need immense computational power that significantly exceeds the capability of
power- and resource-constrained edge devices. EI has been emerged to tackle
the issues mentioned above. EI is the combination of edge computing and AI,
which promises to provide tremendous advantages compared to the conventional
approaches based on cloud, such as privacy-preserving, low-latency, efficient
energy consumption, cost-effective communications, etc [99].

It is generally believed that the 6G networks will adopt ubiquitous AI
solutions from the network core to the edge devices. Nevertheless,
conventional centralized ML algorithms need the availability of a large amount
of centralized data and training on a central server (e.g., cloud server or
centralized machine), which will be a bottleneck in the future ultra-large-
scale mobile networks [100]. Fortunately, as an emerging distributed ML
technique, FL is a promising solution to deal with this challenge and realize
ubiquitous AI in the 6G networks. FL is an ML technique in which creating ML
models do not rely on storing all data to a central server where model
training can occur. Instead, the innovative idea of FL is to train an ML model
at each device (participant or data owner) where data is generated or a data
source has resided, and then let the participants send their individual models
to a server (or aggregation serve) achieve an agreement for a global model
(See Figure 7).

FL can alleviate privacy and security challenges associated with traditional
centralized ML algorithms, as well as guarantee ubiquitous and secure ML for
the 6G networks [101]. The centralized ML technique is contradictory to
ubiquitous ML services, which are promised by 6G. This is mainly due to the
data collection- and data processing-related overheads in centralized ML
techniques. As a result, distributed ML techniques, mostly FL, in which all
training data is located in remote devices locally, are required for future
communication systems. FL is showing itself to be an accelerator for the
extension of privacy-sensitive applications/services. However, despite the
considerable potential advantages of FL for the 6G networks, FL is still in
its infancy and encounter various challenges for fully operationalize in the
6G networks.

Figure 7: A typical federated learning architecture.

The main challenges facing FL in the 6G era include significant communication
cost for model updating and aggregation, privacy concerns associated with
gradient leakage attacks and membership inference attack [102], security
concerns resulted from heterogeneous and various data owners (e.g., data
poisoning attacks), and the model training and inference concerns caused by
the ultra-large-scale of 6G networks. It is envisioned that an enormous number
of heterogeneous devices and communication technologies will be deployed in
the 6G networks; hence, it is crucially important to improve the communication
efficiency in FL algorithms to reduce the number of times the aggregation
server gets gradients from the participant devices. Most importantly, it is
necessary to develop privacy-enhancing mechanisms in FL as the current
techniques proposed for improving privacy in FL, e.g., homomorphic encryption
and secure multiparty computation, can not deal with the attacks mentioned
above [103].

###  VI-E Quantum Communications

Motivated by the great potential of parallelism showed by QC, the QC-assisted
communications field has gained lots of interest. This is mainly due to the
fact that quantum communications has a strong potential to meet the stringent
requirements of 6G such as massive data rates, efficient computing, and strong
security [104]. Toward this end, technologies such as quantum optical
communications (QOCs), quantum optical twin, quantum communication, and
quantum key distribution (QKD) have been investigated in the literature [105]
[106]. The main idea behind QC-assisted communications is that QC uses photons
(or quantum fields) to encode the data in the quantum state (or qubits) and
transmits qubits from a quantum emitter to a quantum receiver. Using qubits in
communications brings enormous advantages, such as communication security,
high-speed and low transmission losses in optical and radio mediums, lessening
the chance of decoherence, etc. Moreover, QC-assisted communication shows
excellent potentials for long-distance communications. More specifically,
quantum repeaters can be used at long distances to divide the communication
link into multiple shorter middle segments and then correct errors such as
photon loss and operation errors in these segments [107].

Several works have practically implemented the applications of quantum-based
technologies in communication systems and networks. For example, QKD’s
capabilities for building a quantum access network have been investigated in
[108] [109]. Besides, the works have been conducted in [110][111] focused on
implementation and testing quantum switches.

AI is another field that will be revolutionized by QC. The currently available
AI techniques are quite expensive in terms of energy, time, and resources,
especially DL models. This is mainly due to the fact that these techniques use
traditional Boolean algebra-based transistors for processing a massive amount
of data, unusually DL models. In other words, the technological advancement in
chipsets does not grow at the same pace as the AI techniques growing.
Fortunately, computing systems based on quantum principles are a promising
solution to tackle this problem as these systems are significantly faster and
more energy-efficient than their traditional ancestors.

###  VI-F Cell-less architecture

Cell-less communication architecture, also known as cell-free, has been
proposed to deal with performance degradation poses by the cellular networks’
handover process [112]. Under this architecture, a UE can communicate with
cooperative BSs (or access points (APs)) through coordinated multipoint
transmission and reception techniques instead of connecting to a single BS.
Establishing cell-less communications can enhance connectivity and lower the
latency induced by the handover process. Cell-less communications will be
inevitable in the 6G era due to the fast deployment of heterogeneous
communication systems and using several frequency bands, where UEs will
transfer from one network to another network without requiring doing handover
process [46]. The UEs will then choose the best link from the available
heterogeneous links (e.g., THz, mmWave, and VLC) in an automated manner. As a
result, the traditional handover process issues, such as data loss and
handover delays/failures, can be alleviated and achieve better QoS. In other
words, cell-less communications will ensure UEs’ seamless mobility without
overhead because of the handover process.

##  VII 6G Challenges and Future Research Directions

A few potential critical open issues for future research work in the 6G
networks are presented in this section.

###  VII-A Energy Consumption Challenges

In the era of 6G, an unprecedented number of low-power IoT devices and
battery-less smartphones will serve over 6G to realize IoT. As a result, super
energy-efficient techniques will be fundamental to guarantee the QoE. The
energy-related issue is much more notable in smartphones as the current
battery life for smartphones without charge is almost one day, which will be
problematic for the development of cellular communications. To deal with this
challenge and comprehensively improve the 6G network performance so as to
serve more end devices, designing effective power supply mechanisms with novel
signal processing techniques is necessary. Several energy supply techniques,
especially wireless energy harvesting-based techniques, can be adopted in the
6G network. In particular, the design of low-density channel codes and energy
efficient modulation techniques are useful. Energy-efficient communication
paradigms also can be investigated, such as symbiotic radio (SR). Furthermore,
energy management optimization in the future cellular network is another
promising technique to create a trade-off between energy demand and supply
dynamically.

###  VII-B AI-related Challenges

It is undoubtable that ML is an integral solution in 6G, but there are some
challenges that should be considered. AI tasks usually generate a heavy
computational load and are often designed, trained, and used at servers with
task-customized hardware. Considering the rapid proliferation of smart-
physical end devices, it is envisioned that a vast number of AI applications
will be designed and used by these devices at the network edge. However, given
the current mobile networks, user privacy, data security, wireless link
capacity/latency are the main concerns of mobile AI-based applications.
Regarding privacy and security, the emergence of decentralized machine
learning techniques is a promising possibility to preserve the users’ privacy
and protect sensitive data. FL tries to create a joint ML model by training an
algorithm on the data placed at several distributed sites. In FL, each
participant (data owner or client) trains a local model and sends the model
weight updates to a central server (a.k.a. aggregation server) instead of
sending raw data. FL offers multiple advantages. For example, it preserves the
users’ privacy and protects the security of data. Moreover, FL allows various
participants to train an ML model in a collaborative manner to achieve a
better model compared to what they can achieve alone.  ML can be integrated by
6G in two aspects including:

  * •

ML on 6G networks: In this aspect, distributed ML techniques e.g.FL and split
learning are performed on 6G network infrastructures for especial tasks
e.g.EI.

  * •

ML for 6G: In this aspect, ML techniques are used as decision-makers for
networking solutions to provide automated network infrastructure establishment
and maintenance. In a more depth aspect, the ML solutions for 6G can be
performed in a distributed manner, although most of existing solution in this
aspect working as a centralized model [113]. One of the most important aspects
of ML for 6G is resource management.

Nevertheless, both aspects are resource-hungry tasks, but they have two goals.
ML on 6G networks try to allocate more idle resources to ML tasks in an
efficient manner, but an optimal ML for 6G solutions can establish the 6G
network with the lowest resource consumption. To achieve both goals, available
resources should be separated between these two aspects in an efficient manner
that is a complex task. In other words, it is unaffordable to use a big volume
of available resources for managing 6G networks and its available resources as
they finally should be allocated to ML tasks for especial purposes.

###  VII-C Terahertz Communications Challenges

THz communications are expected to be a critical enabling technology for the
6G network. However, as we mentioned, THz communications are facing some
challenges, notably severe propagation loss and constrained communication over
long distances. Hence, research communities need to work jointly together to
deal with these challenges and realize THz communications. Fortunately,
several research activities are open-ended, such as THz wireless transmission
by Fraunhofer HHI, Communications, Sensing at Terahertz by NYU WIRELESS, and
ICT-09-2017 Networking research funded by Europe Horizon 2020.

Due to the nature of THz communications’ features, the design of THz MAC also
poses many challenges, including serious deafness problems, complicated
network discovery and coupling operations, and the need for designing
efficient concurrent transmission scheduling techniques. These challenges and
many others are tackled in work have been conducted by Han et al. [71].
Hardware design is another challenge in THz communications. More specifically,
real-world THz communications deployments call for innovations in circuit and
antenna design, as well as miniaturizing current big high-frequency
transceivers. For instance, the antenna size to support joint communication in
mmWave and THz bands may vary (from nanometers to micrometers) and need to be
redesigned.

###  VII-D 3D Coverage Challenges

As mentioned in Section VI-C, the 6G network will support 3D network coverage
because of integrating the terrestrial and non-terrestrial technologies (e.g.,
UAV-assisted and satellite communications). However, this calls for
collaborative research on different aspects of 3D network architecture. First,
air-to-ground 3D channel modeling and measurement for communications are
required. Second, novel topology optimization and network planning methods
(e.g., for the deployment of non-terrestrial BSs/relay nodes) must be
developed. Finally, novel network optimization tools and techniques for energy
efficiency, mobility management, trajectory, and resource management in 3D
network architecture are required.

###  VII-E Security Challenges

Given the new service classes, developed threat landscape, raised privacy
concerns, and new wireless communication methods (e.g., non-terrestrial
technologies), security/privacy is expected to be a critical issue for 6G.
Moreover, network virtualization and softwarization in the 6G era will cause
network security/privacy boundaries to gradually fade. As a result, the
security defects induced by network architecture have become more and more
notable. The increased and closer integration of big data analytics
techniques, AI, and EI may also introduce data security risks at the network’s
edge. The conventional independent security approaches will not be practical
for internal network security risks (network- and access-side) posed by
enabling technologies, new use case scenarios, and service classes. Hence, it
is crucially important to develop the conventional approaches and think about
new security methods [114][37].

In the 6G era, new security techniques, e.g., integrated network security,
will complement traditional physical layer security techniques, especially if
they consider the requirements such as tight security levels and low
complexity. Towards this end, the extension of some of the previously proposed
PHY layer security methods can be used for 6G. For example, PHY layer security
mechanisms for mmWave communications can be adopted for Terahertz
communications. As mentioned in [114], one can refer to authentication-,
access control-, communication security-, data encryption-related technology
as the key security enabling technologies for 6G.

Another envisioned security challenge in 6G is related to the continuing
growth of IoT devices proliferation. At present, the most prevalent IoT
communication protocols are including 6LoWPAN, short for IPv6 over Low -Power
Wireless Personal Area Networks, IEEE 802.15.4, LoRa, and . These
communication technologies use cryptographic algorithms such as Elliptic Curve
Encryption (ECC) and Advanced Encryption Standard (AES) as security
mechanisms. However, with the emergence of new communication technologies,
e.g., QC-assisted communications and the growth in end devices’ computational
power, the traditional IoT protocols will not be secure [115].

###  VII-F Tactile Internet Challenges

Incorporating control, touch, communication, and sensing capabilities into a
shared real-time system pose a crucial issue in accomplishing the tactile
Internet. Despite using virtualization and softwarization technologies and MEC
to achieve the low latency requirements, the tactile Internet is still in its
infancy and further research is needed to solve some open technical
challenges, such as physical layer challenges. Moreover, to alleviate
signaling overhead and air interface latency, taking into account several
other issues, including optimal waveform selection algorithms, robust
modulation techniques, intelligent control plane, Control and User Plane
Separation techniques, should is essential. Scalable routing mechanisms and
adaptive network coding schemes is also worthy further research as they can
reduce end-to-end delay. Besides, security flaws are among the main concerns
about tactile Internet-based use case scenarios. Hence, providing an effective
security mechanism to deal with malicious activities is crucial for realizing
the tactile Internet.

###  VII-G Random Access Protocols

The proliferation of IoT devices calls for providing wireless solutions that
can transfer data in a reliable/energy-efficient/spectral-efficient manner.
However, this is not a trivial task, mostly when an IoT system consists of a
vast number of IoT devices. This is mainly due to the fact that the IoT
devices send/receive data packets sporadically, and unpredictably, and
consequently makes it challenging to design an effective resource allocation
mechanism. Random Access (RA) protocols have been proposed to deal with this
challenge, where they can decline the cost of communication in terms of
wireless resources. The majority of RA protocols that have been employed in
the existing wireless solutions, such as 5G and LoRaWAN, are not optimal in
the networks with a massive number of nodes granting access. To help deal with
this situation, the cooperation opportunities between modern RA methods with
technologies such as NOMA, Orthogonal frequency-division multiplexing (OFDM),
massive MIMO, and sparse signal processing would lead to more efficient
wireless systems [116].

###  VII-H Privacy concerns in the future smart services

The 6G networks are envisioned to offer ubiquitous and unlimited network
access for many users and MTC devices. This seamless connectivity is a
prominent supporter of future smart-based services such as smart environments,
homes, health, industries, cities, utilities, government, etc. As an example,
for a given smart-based service, one might refer to a smart lighting system
for a home, which can provide a more efficient way of lighting in terms of
energy consumption and cost. However, such systems will use/share sensitive
and private information, e.g., occupancy time, household information, habits,
and preferences. Indeed, using this de-identified data by a provider to
deliver smart services may be a double-edged sword in many scenarios rather
than absolutely bring benefit. Given the technological advancements and the
availability of rich data flows, another privacy-related challenge is the need
for a clear definition of de-identified data. This is especially important
when it comes to introducing measures for determining the privacy and
sensitivity level of data in a dataset on any occasion. Integrating the
blockchain technology can be considered as a potential solution to improve the
privacy of 6G, but to the best of our knowledge, there are few efforts to
apply the blockchain in 6G [117].

###  VII-I Green 6G

6G is expected to integrate terrestrial wireless networks with space, aerial
and underwater communications to realize connectivity in 3D coverage. Indeed,
the primary objective of 6G is to ensure anytime anywhere network connectivity
for a vast number of IoT devices and users. These devices/users impose diverse
QoS requirements, need for handling massive/heterogeneous amount of traffic,
and have very low power consumption through design and building energy-
efficient wireless communication protocols, computing, and
transmitter/receiver technologies. Besides the trends and innovative
technologies (i.e., the evolutionary and revolutionary technologies) that we
mentioned throughout the paper, green communication and green computing will
be among the next generation of wireless networks’ primary goals. This is
mainly important for reducing overall power consumption and operating costs,
consequently can bring positive effects on environmental and business aspects.
Towards this end, making a shift from self-organizing networks to is an
increasing trend in the past few years that can be applied in 6G networks.

###  VII-J Network Management and Orchestration (NMO), NTMA, and 6G

6G is considered one of the most important network infrastructures for IoT
networks. Due to the IoT applications, massive-scale IoT networks make
significant challenges in NMO and NTMA techniques [118]. As the former
techniques are used to organize the network infrastructure e.g.fault
management and network configuration management [77], the latter methods are
broadly used to evaluate networking performance in different aspects
e.g.performance management and security management. Both techniques can highly
affect the QoS and QoE which are the crucial factors in 6G. To the best of our
knowledge, there is a shortage of effort to overcome these challenges in 6G,
especially in complex 6G network infrastructures e.g.D2D-enabled 6G and
massive-scale IoT based 6G networks. During the last decade, ML techniques are
proposed to overcome the challenges of NMO [119] and NTMA [66], but there is a
considerable lack of research in these fields for 6G that can be considered as
a future research direction.

##  VIII Conclusion

In this paper, we studied 6G as the next communication paradigm for IoT. We
have first discussed the need for 6G networks. Then, we have introduced the
potential 6G requirements and trends, as well as the latest research
activities related to 6G. Furthermore, the key performance indicators,
applications, new services, and the potential key enabling technologies for 6G
networks have presented. Finally, we have presented several potential
unresolved challenges for future 6G networks.

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