#  6G Survey on Challenges, Requirements, Applications, Key Enabling
Technologies, Use Cases, AI integration issues and Security aspects

Muhammad Sajjad Akbar1, Zawar Hussain12, Quan Z. Sheng1 and Subhas
Mukhopadhyay3  1School of Computing, Macquarie University, Australia
Email: [muhammadsajjad.akbar, michael.sheng<EMAIL_ADDRESS>2School of Computer Science and Engineering, University of New South Wales,
Australia
Email<EMAIL_ADDRESS>3School of Engineering, Macquarie
University, Australia

###### Abstract

The fifth-generation (5G) network is likely to bring in high data rates, more
reliability, and low delays for mobile, personal and local area networks.
Alongside the rapid growth of smart wireless sensing and communication
technologies, data traffic has significantly risen, and existing 5G networks
are not fully capable of supporting future massive data traffic in terms of
services, storage, and processing. To meet the forthcoming challenges, the
research community is investigating the Terahertz-based sixth-generation (6G)
wireless network which is supposed to be offered for industrial usage in
around 10 years. This is right time to explore and learn about various 6G
aspects that will play a key role in successful execution and implementation
of 6G networks in future. This survey provides a review on specification,
requirements, applications, enabling technologies including disruptive and
innovative, integration of 6G with advanced architectures and networks like
software defined networks (SDN), network functions virtualization (NFV),
cloud/fog computing etc, artificial intelligence (AI) oriented technologies,
privacy and security issues and solutions, and potential futuristic use cases:
virtual reality, smart healthcare and Industry 5.0. Furthermore, based on the
conducted review, challenges and future research directions are highlighted to
aid the deployment of 6G networks.

###### Index Terms:

5G, 6G, artificial intelligence, automation,machine learning, security,
massive connectivity, virtual reality, terahertz.

##  I Introduction

6th generation (6G) networks are considered as a successor to 5G networks and
will be capable of using higher frequencies than frequencies used by 5G
networks, and also delivering significantly higher capacity and very low
latency [30, 123]. One of the milestones of 6G is to provide one-microsecond
transmission latency which will be 1,000 times faster than one millisecond
available in 5G networks [77]. 6G networks will improve the performance of
different technological areas including location awareness, imaging, and
telepresence, etc. 6G networks will also incorporate the machine learning and
artificial intelligence (AI) technologies, and these technologies will make
the 6G-autonomous systems. 6G is expected to provide high transmission rates
of up to 1 terabyte per second (Tbps) using the Terahertz spectrum.

Such a level of network capacity with very low latency is exceptional and it
will also increase the performance efficiency of some existing applications.
Furthermore, the high frequencies in 6G networks will lead to a higher
sampling rate which will increase the transmission quality of wireless
devices. Figure 1 shows the THz spectrum with the range 100 GHz to 3 THz which
is going to be used as 6G’s spectrum.

Figure 1: TeraHertz Spectrum [121]

It is expected that 6G will be commercially available in 2030. The research
and industrial efforts have already been started. The research activities for
6G networks are initiated under the International Mobile Telecommunications
2030 project [29]. China already initiated the project Broadband
Communications and New Networks [61] for 2030. The European Commission Horizon
2020 program has funded various projects for 6G networks like TERRANOVA (Tb/s
wireless connectivity using THz band with the optical network) [18]. The
Semiconductor Research Corporation in the United States funded a research
project on future communication over THz band in cellular architecture [123].
The Federal Communications Commission (FCC) has begun investigating 6G
networks using the THz band. Similarly, the International Telecommunication
Union (ITU) has established a research group focusing on network requirements
for the year 2030. Finland funded 6Genesis [28] and arranged the in 2019. Each
of these research activities shows how important it is to explore beyond 5G to
fulfill future quality of service (QoS) requirements [94].

Similar to the 5G which, is facing various deployment challenges, there will
be many new challenges for 6G deployment as well. One of the prominent
challenges is the development of commercial transceivers that operates on THz
frequencies. The electronics and networking component providers need to play
their role by introducing new innovative transceiver designs. It is expected
that in the near future, trillions of sensors will be implanted into cities,
homes, and industries, and these sensor-based systems will create a plethora
of new applications, therefore, smart transceivers’ design could play an
important role to manage these huge sensor-based implementations.

Figure 2: Survey Taxonomy

This survey provides a comprehensive review of the various aspects of the 6G
technology and the challenges in its adoption. We have explored major
databases like ACM Digital Library, Science Direct, IEEE Xplore, and Scopus
and have selected papers from well-known publishers such as IEEE
Communications Surveys Tutorials, IEEE Access, IEEE Vehicular Technology
Magazine, IEEE Transactions on Wireless Communications, and IEEE Sensors. We
have searched for works related to 6G using the search term such requirements,
use cases, advanced architectures, integration, SDN, IoT, big data, ML and
security issues, etc. We have focused on recent articles which are published
during the last decade and selected around 50 papers for our review.

We address four key areas for 6G: potential use cases, enabling technologies,
AI technologies, and applications. Figure 2 presents the taxonomy of this
survey. Potential use cases are further discussed in three aspects including
virtual reality, smart healthcare, and Industry 5.0. Enabling technologies are
sub-divided and discussed into three main categories: innovative
architectures, disruptive, and intelligence integration. AI technologies are
discussed in terms of big data, optimization of existing algorithms, and cases
of intelligent communication. Various 6G-based applications with respect to
challenges, security and privacy, and distributed ML are also discussed at the
end of this work. We review state-of-the-art works to address all these
aspects of 6G networks and provide the user with a comprehensive overview of
all the opportunities, challenges, and requirements of the 6G network. To ease
the reading, a list of acronyms used throughout this paper is given in Table
I.

The key contributions of this work are as follows:

  * •

This work links the various technologies and architectures (such as
Blockchain, Industry 5.0, augmented reality (AR), mixed reality (MR), virtual
reality (VR), mmwave, Unmanned Aerial Vehicle (UAV), software-defined networks
(SDN) and many more) with 6G networks by addressing their integration issues.

  * •

Most of the previous surveys used only Yes or No for comparing different
factors which hardly provides any details. Unlike the previous surveys, this
work provides a fine-grained comparison like Low (L), Medium (M), High (H),
and No (N) for different factors.

  * •

This survey provides a detailed categorization of various 6G-related aspects.
For instance, use cases are categorised into VR, health, and Industry 5.0,
enabling technologies (disruptive and innovative), and AI (machine learning
(ML) and big data, etc.).

  * •

Suggestions for the integration of various technologies with 6G are provided
in this survey.

  * •

New 6G performance metrics are proposed which were previously ignored in the
literature. These include Industry 5.0, big data, integration (6G and
disruptive technologies), and security and privacy.

The remainder of this paper is structured as follows. Section II discusses the
related work and Section III provides the background for communication
technologies. Section IV describes the 6G key enabling technologies while
Section V provides discussion on AI-enabled technologies for future 6G.
Section VI presents the potential use cases of 6G technology. Section VII
presents AI-based 6G applications. Section VIII discusses the challenges in 6G
network and the future research directions. Section IX concludes this work.

TABLE I:  List of acronyms and the corresponding definitions Acronyms |  Definition |  Acronyms |  Definition
---|---|---|---
1/2/3 /4/5/6 G |  First/Second/Third /Fourth/Fifth/Sixth Generation |  Tbps |  Terabyte per second
AI |  Artificial Intelligence |  THz |  Tera Hertz
3GPP |  3rd Generation Partnership Project |  GHz |  Giga Hertz
mmWave |  millimeterWave |  FCC |  Federal Communications Commission
QoS |  Quality of Service |  NMR |  Nordic Mobile Radio System
GSM |  Global System for Mobile Communication |  SMS |  Short Messaging Service
EDGE |  Enhanced Data Rates for GSM Evolution |  GPRS |  General Packet Radio Service
UMTS |  Universal Mobile Telecommunication System |  CDMA |  Code Division Multiple Access
D2D |  Device to Device |  LTE |  Long-Term Evolution
ML |  Machine Learning |  VR |  Virtual Reality
MR |  Mixed Reality |  AR |  Augmented Reality
UAV |  Unmanned Aerial Vehicle |  eMBB |  Enhanced Mobile Broadband
SDN |  Software Defined Network |  URLLC |  Ultra-Reliable low Latency Communications
FSO |  Free Space Optical |  IoT |  Internet of Things
BCI |  Brain Computer Interaction |  IoE |  Internet of Every Thing
HCI |  Human Computer Interaction |  M2M |  Machine to Machine
DAS |  Distributed Antenna Systems |  UGV |  Unmanned Ground Vehicle
MIMO |  Multiple-Input-Multiple-Output |  UUV |  Unmanned Underwater Vehicle
VLC |  Visible Light Communication |  RF |  Radio Frequency
NFV |  Network Functions Virtualization |  WIET |  Wireless Information and Energy Transfer

##  II Related Work

There are several surveys in the literature which have reviewed the research
work conducted in the area of 6G. These surveys have focused on different
aspects of 6G technologies. This section provides an overview of some of the
most recent surveys and also provides a comparison with our work.

The survey [95] discusses the 6G-based features and challenges for Cellular
IoT which can connect enormous physical and virtual objects to the Internet
using cellular networks. These issues include delay and throughput provision
for bandwidth-hungry applications like holographic communication etc. The
survey also discusses various aspects of 6G networks like needs, requirements,
applications, key performance indicators, and the potential key enabling
technologies. However, it doesn’t provide the details about security, ML/AI,
and various potential use cases like industry 5.0.

The article [43] provides a comprehensive survey on 6G-enabled massive IoT. A
detailed discussion is provided regarding the driving forces and requirements
of the emerging IoT-enabled applications, alongside the constraints of 5G/6G.
The authors also discussed the 6G core areas that can be linked with IoTs,
these areas include technical requirements, use cases, and trends. The authors
proposed an architecture of IoTs that is supposed to use 6G technologies, this
architecture is called space–air-ground–underwater/sea networks and it’s
incorporated the ML mechanisms. The article lacks the 6G-related discussion
about security, and also various use cases are ignored in the survey including
VR, industry 5.0, big data, and health applications.

The article [110] discusses a vision of the recent paradigm shift of 6G
wireless communication networks and provides performance evaluation metrics
for such a network. The focus of the survey is to provide a comprehensive
analysis of channel characteristics on various 5G and 6G frequency channels
including mm-wave, terahertz, and optical wireless communication. These
channel characteristics are discussed in application scenarios of satellite,
UAV, maritime, and underwater. The future challenges of 6G channel
measurements and models were pointed out; however, the article lacks details
on 6G-based disruptive technologies. The article also ignored the security
challenges on these channels which is a critical aspect of 6G networks.

The articles [81, 40] review the generations of mobile wireless technologies
including requirements, advantages, and disadvantages. The authors focused on
the channel modeling, especially the channel modeling options of the 6G
networks. The cost-benefit analysis is also provided. However, the articles
lack the discussion of the integration of the channel models with 6G networks.

Free space optical (FSO) [37] is an emerging technology for 6G networks and
has the potential to resolve the bandwidth bottleneck. The authors discussed
the issues caused by the fog’s attenuation to FSO and they proposed an
empirical prediction model to calculate its impact.

The goal of the survey [109] is to provide the state of UAVs and understand
the telecommunications-related issues that need to be resolved. A schematic
architecture is described along with the implementation proposed for UAVs. The
author provides an analysis of aviation-specific Automatic dependent
surveillance-broadcast (ADS-B) to 5G/6G cellular networks. The authors only
discussed the fundamental requirements of 6G networks for UAVs; the detailed
use cases and security aspects must be provided.

In this survey [12], the authors presented a framework for developing flexible
radio access technologies (RATs) for 6G networks and their related
requirements. The framework provides flexibility for the selection of RATs.
This framework also highlights the inefficiency of the fixed waveform
parameterization of 5G/6G numerologies. However, the article lacks a
discussion about the security issues of RATs.

The survey [87] provides the evolution of wireless networks in terms of the
5G/6G heterogonous networks architecture along with its requirements and
challenges. Further, the authors also discussed the integration of emerging 6G
technologies including Software-Defined Networking (SDN) and Machine to
Machine Communications (M2M) in the context of 5G/6G networks. However, the
survey lacks a detailed discussion about the integration of these domains with
6G networks.

The authors in [89] gives a detailed survey on the Smart grid based on IoT
frameworks. Various performance metrics are identified to evaluate such a
framework, these metrics include delay, high throughput, power management,
security, etc. The authors concluded that a lack of effort was being done to
secure IoT-based frameworks and suggested that cyber security should be
incorporated as a compulsory aspect of these systems in the future. The survey
lacks the discussion about 6G applications, requirements, integration of IoTs
with advanced network architectures, etc.

The survey [119] presents a detailed discussion on architectures and
requirements of 6G networks. The authors evaluated the recommended
architectures based on performance parameters including latency and throughput
to check their suitability for 6G networks. The evaluation is done with
various applications including senses like 3D video and sensitivity (touch,
smell, texture, etc.). The authors concluded that 6G networks with mentioned
applications required Super Ultra-Low Latency, High-Precision Latency, and
ultra-delays.

The survey [70] discusses a comprehensive review of a cellular system in the
context of software-defined 5G/6G networks. A detailed discussion is provided
regarding application scenarios, challenges, and key technologies for 6G-based
SDN. The authors also discussed the requirements of advanced sensors and
monitoring technologies that will be used with upcoming 6G networks. Various
important aspects of 6G networks are ignored like cyber security, industry
5.0, big data and ML, etc.

The survey [86] provides a detailed discussion of security and privacy aspects
of the 6G networks by visualizing various use cases; however, the authors did
not provide enough discussion for the ML, AI, key technologies, and industry
5.0.

The article [104] presents a survey on several ML techniques linked to
networking, transmission, and security aspects of the vehicular networks. The
authors also visualize the ways of enabling AI toward a future 6G vehicular
network, these visualized areas include intelligent radio (IR), network
intelligence, self-learning with proactive exploration, etc. AI mechanisms
will enable ultralow delay, better reliability, security, and high bandwidth.

This article [118] discusses the future trends in the evolution of ubiquitous
connectivity in rural areas and linked the requirements to the 6G networks.
The authors also discussed the application and requirement aspects of the 6G
network; however, the article lacks a discussion about ML and security
aspects.

Table II provides the comparison of the 6G’s surveys. This comparison is
conducted based on important and futuristic performances metrics, some of
which (virtual reality (VR), health, machine learning (ML), architectures,
trends, and disruptive technologies) are extracted from literature and some
are proposed metrics from our side like integration, security and privacy,
Industry 5.0, and Big data. It can be seen from Table II that most of the
surveys lack in the areas like security, integration, Big data, and industry
5.0. Almost all of them discuss the ML, architectures, and challenges in
detail. Our survey provides a detailed discussion of all these comparative
metrics.

TABLE II:  Summary of existing 6G surveys Survey | Year |  Requirements Challenges |  Use Cases (VR, Health, Industry5.0) |  Enablers (Disruptive, Innovative) |  AI (ML, Architectures, Big data, Trends) |  Technological Integration |  Security Privacy
---|---|---|---|---|---|---|---
[40] | 2015 | M | (N, H, N) | (L, L) | (L, M, M, H) | L |  L
[81] | 2015 | M | (L, M, N) | (L, M) | (N, L, M, M) | L |  L
[109] | 2016 | H | (N,M,N) | (L, M) | (H, H, L, M) | M |  L
[37] | 2016 | M | (N, M, N) | (L, L) | (M, L, N, L) | L |  L
[87] | 2017 | M | (L, M, L) | (M, L) | (L, H, L, L) | N |  L
[12] | 2017 | M | (L, M, L) | (L, L) | (H, M, L, M) | L |  L
[119] | 2018 | L | (L, M, L) | (M, L) | (L, H, L, L) | N |  L
[89] | 2018 | M | (L, M, L) | (L, L) | (H, M, L, M) | L |  L
[70] | 2019 | L | (L, M, L) | (M, L) | (L, H, L, L) | N |  L
[104] | 2019 | M | (L, M, L) | (L, L) | (H, M, L, M) | L |  L
[118] | 2020 | L | (N,M,N) | (L, M) | (H, H, L, M) | M |  L
[110] | 2020 | M | (N, M, N) | (L, M) | (M, L, N, L) | L |  L
[95] | 2021 | H | (L, H, N) | (L, M) | (H, M, M, H) | L |  L
[43] | 2021 | L | (H. M, N) | (L, M) | (H, L, M, M) | L |  L
Our survey | 2022 | H | (H, H, H) | (H, H) | (H, H, H, H) | H |  H

##  III Background, Specification and Requirements

Before jumping to the 6G requirements, it is important to analyze the
available generations. In the following section, we provide a brief revision
of all the generations with their strengths and limitations. We also discuss
the specifications and requirements for 6G networks.

###  III-A Background of Communication Generations

Since the beginning of the first analog communication system in the early
eighties, a new mobile communication generation has been launched after every
ten years. In the following sections, we go through these generations and
present their strengths and limitations.

####  III-A1 First Generation

First Generation (1G) provided the service of voice calling with a large
network coverage. A partial fax service was also introduced in 1G networks. 1G
networks faced the issues like incompatibility of different market standards.
For instance, at that time USA was using an advanced mobile phone system;
whereas several European countries adopted the nordic mobile radio system
(NMR). Both standards were incompatible with each other. Digital speech codecs
were not introduced in the 1G era [102].

####  III-A2 Second Generation

The global system for mobile communications (GSM) is considered the key
standard of second-generation (2G). It began in Europe with a French name
Groupe Speciale Mobile, and within years, GSM had around 90 percent of the
market share. In GSM, newer voice codecs improved the voice quality. One of
the key features of the GSM was the short messaging service (SMS). In
addition, GSM also launched digital data services [99]. Initially, a data rate
of 9.6 kb/s was achieved; later, the data rate was further improved to 200
kb/s with the launch of general packet radio service (GPRS) and enhanced data
rates for GSM evolution (EDGE). 2G had various challenges that include low
data rate, high cost, delay, etc.

####  III-A3 Third Generation

Several European regulators agreed to establish a new approach to granting
licenses by the mid and end of the 1990s. It was expensive to buy a spectrum
from auctions in open markets. Even after the license was granted, no standard
came out. However, after several years of license granting, the universal
mobile telecommunication system (UMTS)-based cell phones came into the market.
UMTS used code division multiple access (CDMA). Initially, UMTS provided the
data rates up to 364 kb/s which was far less than the expected rate; however,
in the later modifications, higher data rates were achieved. UMTS could not
provide the expected performance due to bandwidth issues. Therefore, operators
decided to move towards 4G long-term evolution (LTE) [88].

####  III-A4 Fourth Generation

While working with UMTS, operators learned lessons and they were not fully
confident to invest in the fourth generation (4G). Also, various technologies
were introduced regarding spectrum efficiency which provided the flexibility
for operators to select the appropriate option according to their
requirements. Most of the LTE standards kept upgrading 3G UMTS to develop 4G
communications. The LTE aimed at simplifying the existing UMTS architecture
from circuit and packet switching to IP-based architecture. LTE provides up to
300 Mb/s and uploads data rates of up to 75 Mb/s. LTE offers low latency (up
to 5 ms) with mobility support. It uses orthogonal frequency division multiple
access (FDMA). Although, 4G provides solutions to several problems faced by 3G
like low data rates and higher latency values; 4G faced other issues such as
performance management for the rapid growth of the number of devices that
required ubiquitous connectivity and higher data rates [54].

####  III-A5 Fifth Generation

The fifth-generation (5G) is the new generation of cellular networks.
According to the 3GPP project, if a device is using 5G new radio (5G NR)
software, it is called a 5G device. 5G network is supposed to provide data
rates of up to 20 Gb/s. The 5G technology promises to offer a latency between
1 to 5 ms. The speed of 5G in the sub-6GHz band will be greater than the 4G
with a similar number of antennas and spectrum. Overall, 5G uses a high-
frequency band (above 30 GHz). In recent years, mmWave appeared as one of the
potential candidates for 5G by providing data rates in gigabits/sec. It uses a
spectrum between 30 and 300 GHz and corresponds to wavelengths between 10mm to
1mm. The characteristics of mmW includes high bandwidth, short-wavelength/high
frequency, and high attenuation [31]. High-power levels are required to
overcome the huge path loss, which is expected in mmW communications. The
coverage is up to 20 meters at 60 GHz. IEEE 802.15.3c and IEEE 802.11ad
standards provide the support of mmW. Moreover, 5G will deliver the services
like device-to-device (D2D) connectivity and massive machine-to-machine
communications, etc. 5G bands have several challenges including poor foliage
penetration, atmospheric and free space path loss, and high deployment cost.

Table III shows a performance comparison for different generations of the
communication systems.

TABLE III: Performance comparison among 4G, 5G and 6G  Characteristics |  4G |  5G |  6G
---|---|---|---
Device peak data rate |  1Gbps |  10-20 Gbps |  1Tbps
Latency |  100 ms |  5-10 ms |  10-100 micro sec
Maximum spectral efficiency |  15 bps/Hz |  30 bps/Hz |  100 bps/Hz
Mobility |  350 Km/hr |  500 km/h |  up to 1000 km/h
Network energy efficiency |  1x |  10-100 x of 4G |  10-100 x of 5G

###  III-B Specifications and Requirements

There are several performance trade-offs in reliability, delay, throughput,
and power consumption for 5G networks and these trade-offs give an impression
that 5G could not fulfill the market challenges beyond 2030. The
characteristics and features of 6G networks have the potential to overcome
these challenges. The main features of 6G networks include high data rates,
energy efficiency, reliability, and intelligent decision-making per device as
well as a whole network at the global scale. The 6G networks are supposed to
provide services including enhanced mobile broadband (eMBB), ultra-reliable
low latency communications (URLLC), massive machine-type communication (mMTC),
and AI and machine learning integration. URLLC, promises to provide a delay of
less than 1 ms [105]. Furthermore, the 6G system makes use of energy
harvesting techniques so that ultra-lifetime could be provided to the devices.

For the satellite integrated network, 6G is expected to provide improved QoS
for satellite communication. 6G aims to make a single wireless operating
network by integrating the terrestrial and satellite networks. 6G will provide
massive connectivity to wireless devices in the form of IoE; however, such
huge connectivity creates challenges like quick processing and transmission
delays. To cater to this, 6G will make use of extensive AI techniques at each
phase of the communication including signaling, forwarding, and decision
making [68].

Ubiquitous connectivity is considered one of the key requirements for 6G
networks. In a broader aspect, drones and the satellites in orbits will help
to create super-3D connectivity for 6G to provide ubiquitous communication
[68]. 5G successfully uses the small cell communication networks to improve
QoS including throughput, energy, signal quality, and spectral efficiency;
however, it lacks the integration of disciplines like AI, ML, and automation
[23, 97, 75].

6G networks will incorporate the approach of small cell networks in a larger
aspect. Due to massive device connectivity, the future 6G networks will be
ultra-dense with heterogeneous connectivity characteristics.

In recent years, millimeter wave (mmW) appeared as one of the potential
candidates for 5G by providing data rates up to gigabits/sec and it could be
also useful for the initial implementation of 6G networks. It uses a spectrum
between 30 and 300 GHz and corresponds to wavelengths between 10 mm to 1mm.
The characteristics of mmW include high bandwidth, short-wavelength/high
frequency, and high attenuation [69, 83]. Huge path loss is expected in mmW
communications, to recover from these path losses, high power levels are
required. Due to high attenuation from solid material (bricks and buildings),
mmW requires line of sight (LoS) for efficient and reliable communication. The
interference levels in mmW communication are much lower than the 2.4 GHz and 5
GHz. Multiple-antennas solutions in mmW allow the transmission to use narrow
beams which help to reduce the attenuation and the interference [113, 58, 45].
There have been several standardization activities for mmW MAC in the 60 GHz
band. Most of these standardization efforts are for personal and local area
networks under IEEE 802.15.3c and IEEE 802.11ad. IEEE 802.15.3c standard also
known as piconet specifies the mmW by supporting a high data rate over 2 Gbps
in the 60 GHz band. Among a cluster of IEEE 802.15.3c-based devices, one will
be selected as the piconet coordinator (PNC) which manages the synchronization
among devices by broadcasting beacon messages. The devices content for the
time slots using carrier sense multiple access/collision avoidance (CSMA/CA)
and sending data using time division multiple access (TDMA) [108, 116]. The
IEEE 802.11ad introduced several modifications at MAC and the physical layer
of the existing IEEE 802.11 standard to enable mmW support. It claims to
provide a 6.75 Gbps data rate. The coordinator uses a superframe structure to
manage the channel access of the connected stations which is composed of
beacons, contention access period (CAP) and contention-free period (CFP) [1,
4, 7, 3, 5].

It is important to understand the performance computation process for various
technologies under the 6G network could be different and dependent on a
specific technology. As an example, a discussion is provided regarding the
performance evaluation of a 5th generation’s medium access control (MAC)
mechanism of the IEEE 802.15.3C standard in terms of end-to-end delay (ED) and
maximum throughput (MT). The purpose is to understand how much time it takes
for a CTA request in the worst-case scenario. The ED can be calculated as
given in Equation (1) [6, 7, 2]:

$ED=T_{frame}+T_{ACK}+T_{CH}\hskip 113.81102pt(1)$

Where $T_{\mathrm{frame}}$ represents frame transmission time for a CTA
request frame. Further, $T_{\mathrm{frame}}$ can be computed as given in
Equation (2):

$T_{frame}=T_{Preamble(PHY)}+T_{Header(MAC+PHY)}+T_{Payload}\hskip
4.26773pt(2)$

Where $T_{\mathrm{Preamble}}$ is the duration of PLCP preamble,
$T_{\mathrm{Header}}$ is the duration of PLCP header and
$T_{\mathrm{Payload}}$ is the duration of the payload. These durations are
given in the IEEE 802.15.3C standard.

$T_{\mathrm{ACK}}$ represents the time duration of the ACK, in this case, ACK
duration computed as given in Equation (3):

$T_{ACK}=T_{ImmACK}+2SIFS\hskip 113.81102pt(3)$

Where $T_{\mathrm{ImmACK}}$ is time duration of the immediate ACK and can be
computed as given in Equation (4):

$T_{ImmACK}=T_{Preamble}+T_{Header}\hskip 91.04872pt(4)$

The ACK of the ImmACK has only MAC header and not a payload as each packet is
expected to be acknowledged immediately. $T_{\mathrm{CH}}$ represents the time
to access the channel, which is computed as given in Equation (5):

$T_{CH}=(RC*BIFS)+(BC*pBackoffSlot)\hskip 28.45274pt(5)$

Where RC is the retry counter in the backoff process and its value will be 3
in the worst case as default, BIFS is the backoff IFS and it is calculated by
Equation (6):

$BIFS=pSifsTime+pCcaDetectTime\hskip 56.9055pt(6)$

The values of pSifsTime and pCcaDetectTime are given in the Table I mentioned
by the in the IEEE 802.15.3C standard. BC is the backoff counter calculated as
given in Equation (7):

$BC=Rand(0,BW)\hskip 142.26378pt(7)$

BC is computed using a random function that finds a random integer value
between zero and BW (backoff window). The value of BW is given in Table IV.

TABLE IV: Timing and space parameters mentioned by IEEE 802.15.3C standard [107] PHY Parameter | Duration HSI ($\mu$s)
---|---
pSIFSTime | 2.5
pCcaDetectTime | 2.5
pBackoffSlot | 5
T_Preamble | 1.31
T_Header | 0.44
Backoff Windows | [7, 15, 31, 63]
Retry Count | 0 to 3
CAP duration ($\mu$s) | 0 to 65,535
Superframe duration ($\mu$s) | 0 to 65,535
MAC header (bytes) | 10
PHY header (bytes) | 48
Acknowledgement (bytes) | 10
Beacon packet (bytes) | 100
Data frame (bytes) | 512 to 8,388,608
Channel data rate (Gbps) | 1.5, 3, 5

The maximum throughput (MT) is defined as a ratio of transmitted information
in bits to the transmission duration. Throughput is defined as the ratio of
payload size (x) to the total time required to transmit the payload, in the
case when there is no priority set the maximum network throughput can be
computed as given in Equation (8):

$MT=X/ED\hskip 176.407pt(8)$

In this regard, multi-tier network architecture will help to manage these
ultra-dense networks in 6G. To manage the huge traffic in 6G networks, high-
capacity backhaul networks are required [8]. This can be possible by using
free-space optical (FSO) systems and a fast optical fiber network. Overall, 6G
wireless networks will be able to:

  * •

Provide super-high-definition (SHD) and extremely high-definition (EHD) video
transmission which requires ultra-high throughput.

  * •

Support extremely low latency up to 10 micro-second.

  * •

Manage the Internet of Nano-Things and Internet of Bodies using smart
implantable and wearable devices under very low power consumption.

  * •

Enable efficient underwater and space transmissions to drastically increase
the limits of human activity, for example, deep-sea exploration and space
traveling.

  * •

Support various advanced service practices like hyper-high-speed railway
(HSR).

  * •

Improve 5G applications, like the huge Internet of Things (IoT) and entirely
autonomous vehicles.

  * •

Support enhanced mobile broadband communications, ultra-huge machine-type
communications, enhanced ultra-reliable and low-latency communications, and
long-distance and high mobility communications.

##  IV 6G Enabling Technologies

This section describes the technological enablers that are supposed to provide
the 6G revolution. These key enablers will justify the migration from 5G to 6G
networks. We discuss various technologies including Terahertz (THz), optical
wireless technology, full-duplex communication, channel estimation techniques,
network localization, MIMO, 3D network architecture, network virtualization,
FSO, energy harvesting techniques, blockchain, WIET, and supervised and
unsupervised learning [57, 64, 41, 11, 93, 112, 8, 106, 100, 42, 33, 10].
Based on technological characteristics, we categorize enabling technologies
into three main categories: disruptive, innovative, and intelligence
integration. We discuss these technologies in the following sections.

###  IV-A Disruptive Communication Technologies

Communication technologies like multiple-input-multiple-output (MIMO) and
mmWave are considered key enablers of 5G networks and provide required QoS. To
meet the requirements of 6G networks, 6G will exploit the unused terahertz
band and visible light communications (VLC).

####  IV-A1 Terahertz Communications

The radio frequency (RF) band is nearly exhausted and not enough to fulfill
the 6G requirements. Spectral efficiency can be enhanced by expanding the
bandwidth, which can be achieved by applying THz bands and massive MIMO
technologies. The THz band will play an important role in 6G communication
[106]. The THz band is intended to be the next frontier of high-data-rate
communications. THz communications use the frequency bands between 100 GHz and
10 THz with wavelengths in the 0.03 mm – 3 mm range. ITU-R recommends 275 GHz
- 3 THz bands for the communication of cellular networks [100]. In comparison
with mmWave, THz faces challenges due to ultra-high frequencies. The main
challenges that are hurdles for terahertz’s commercial adoption are the
propagation loss, the molecular absorption at a higher frequency, the high
penetration loss for solid objects, and, the antenna design complexity. In
mmWave, the propagation loss can be controlled using the directional antenna
arrays mechanism. However, some of the frequencies in the terahertz spectrum
are more sensitive to atmospheric molecular absorption. This issue can be
avoided by only using selected frequencies band in Terahertz spectrum [57].

####  IV-A2 Optical Wireless Technology

Optical wireless technology (OWC) is considered a key enabler for 6G
communications; however, it is already being used partially in 4G/5G networks.
Main OWC technologies include light fidelity, optical camera communication,
VLC, and FSO communication [22, 21, 49, 50]. Networks with OWC technologies
have the potential to provide ultra-throughput and very low latency. VLC is
proposed to support RF communications using the light-emitting diode (LED)
luminaries. These devices can switch among various light intensities to
modulate the signal. The research activities on VLC are more progressive than
the terahertz communications. Although, the IEEE 802.15.7 standard is proposed
for VLC, yet not been adopted by 3GPP. VLC has a short-range, requires an
illumination source for communication, and is also sensitive to the noise
created by any light source including the sun [64]. Due to these reasons, it
is mostly recommended for indoor environments.

####  IV-A3 Full-Duplex Communication

Recently, cellular networks are adopting full-duplex solutions which will make
the base station’s transceiver capable of transmitting a signal while
receiving within a limited range, such design is achievable using innovative
design of self-interference suppression circuits [41]. The main innovation is
being achieved by improving the design of the antenna and circuit which
reduces the cross-talk between receiver and transmitter for a wireless device
[44].

In future, technological advancement will be capable of simultaneous
transmission and reception. Such advancements enhance the multiplexing
capabilities of the existing cellular systems in terms of throughput. 6G
network could be very useful for such a full-duplex network in terms of
performance requirements.

####  IV-A4 Novel Channel Estimation Techniques

For cellular networks, channel estimation for beam tracking is considered an
important element for ultra-high frequencies. Therefore, advanced channel
estimation techniques will be highly beneficial in cellular networks. If we
talk about mmWave, design challenges exist for channel estimation techniques
due to complex antenna architectures. A recent research effort is made to
provide channel estimation for mmWave, known as out-of-band estimation, which
provides an improvement in the reactiveness of beam management schemes [11].

####  IV-A5 Sensing and Network-Based Localization

Techniques based on RF signals have been broadly used to empower concurrent
operation of localization and mapping techniques [120]. However, it is still
not adopted for the case of cellular networks. In this regard, 6G will provide
an integrated interface to support localization and mapping mechanisms which
will offer multiple advantages including enhancement in control operations,
less interference and novel services for applications like eHealth.

####  IV-A6 Advanced Multiple-Input-Multiple-Output

Multiple antenna technologies have achieved considerable response from both
academia and industry as they are capable of providing high geographical
coverage and spatial multiplexing which helps to boost spectral efficiency
[39, 15] and could be very useful to achieve the performance goals of 6G
networks. Therefore MIMO is a potential area that can integrate itself with 6G
networks which will increase the capability of 6G network devices in terms of
transmission.

###  IV-B Innovative Network Architectures

6G network offers various innovations to the existing communication
architecture e.g., virtualization. In this section, we will discuss some of
the promising architectural domains.

####  IV-B1 Cell-Less Architecture and Tight Integration of Multiple
Frequencies

The 6G network will create a strong relationship between cells and the user
equipment (UEs) and will create cell-less architecture. This can be
accomplished using dual-band techniques with multiple radios. Such
architectures provide high mobility support with fewer overheads. It will
increase the user capacity by seamlessly transitioning to multiple
heterogeneous links (e.g., to identify the best channel) without any prior
configurations.

####  IV-B2 3D Network Architecture

6G networks will support three-dimensional (3D) visualisation by using
heterogeneous architecture. Communication technologies like drones and
satellites will use these architectures to improve related QoS. Furthermore,
these heterogeneous communication architectures could be easily deployed in
rural and disaster areas [117].

####  IV-B3 Virtualization of Networking Equipment

In 6G networks, network virtualization will be one of the key concepts. The
virtualization will be implemented at different layers i.e., network layer,
MAC layer, and physical layer. It will reduce the management burden from
hardware devices in a cost-effective way. It will create a centralized virtual
controller which will be responsible for the policy and data forwarding [9].

####  IV-B4 Advanced Access-Backhaul Network (FSO) Integration

In 6G networks, technologies are supposed to provide ultra-high data rates
which also require sufficient expansion of backhaul capacity. Furthermore,
future VLC and terahertz implementations will use a massive number of devices,
which need backhaul connectivity with the core network and neighbouring nodes
[93].

In some cases, such as, at remote geographical locations (e.g., sea, space and
underwater), it is not feasible to deploy optical fibre as a backhaul network.
For such scenarios, the FSO backhaul network is a potential communication
system for 6G networks [42, 33, 16]. FSO performs like an optical fibre
network and provides similar communication services. FSO can provide a large
communication range, even greater than 10,000 km. Figure 3 shows an example of
an integrated backhaul link.

Figure 3: Integrated backhaul link [32]

####  IV-B5 Energy Harvesting Strategies for Low-Power Consumption Network

Energy/power harvesting is defined as the process by which energy is extracted
from external resources including solar, thermal, wind, and kinetic energy,
etc., and then preserved for wireless autonomous devices, like wearable
biomedical monitoring sensors. The energy harvesting process is capable of
producing a small amount of energy which is an ongoing challenge for the
industry. One of the oldest applications of energy derived from ambient
electromagnetic radiation (EMR) is known as crystal radio [85].

5G networks do consider energy harvesting techniques; however, not much work
is done on that due to a lack of interest from manufacturers. As 6G networks
deal with a massive number of interconnected smart devices (e.g., IoT devices
) that require energy resources to keep the device alive. There is a need to
identify the suitable energy-efficient mechanisms for such devices [112].

####  IV-B6 Blockchain

Blockchain manages huge datasets in an efficient way [46, 14, 80]. Blockchain
works using the approach of distributed ledger technology. A distributed
ledger’s database operates in a distributed manner by using various nodes.
Every node replicates a copy of the ledger. The nodes can work independently
without involving the centralized node. In the blockchain, the data is managed
in the form of blocks that are connected securely. The blockchain can manage
huge data involved in IoTs-based systems [26]. Hence, 6G capabilities will
support blockchain-based applications in terms of their performance goal.

####  IV-B7 Integration of Wireless Information and Energy Transfer (WIET)

Recently, producing energy from the open environment is one of the potential
areas which can help to increase the lifetime of battery-operated wireless
sensor devices. WIET is recognized as one of the innovative technologies for
6G networks. WIET also utilizes the wireless spectrum for communication. WEIT
can be very useful for battery-less devices which are supposed to operate in
6G networks. The base stations in 6G will be used for transferring power as
Wireless Information and Energy Transfer (WIET) uses the same fields and waves
used in communication systems. WIET is an innovative technology that will
allow the development of battery-less smart devices, charging wireless
networks, and saving the battery life-time of other devices [35, 111].

###  IV-C Integrating Intelligence in the Network

The 6G network complexity in terms of architecture and data demands AI and
machine learning techniques to play their role in the successful execution of
6G services. Although 6G does not specify specific techniques, more likely AI
based on a data-driven model could be used for the successful deployment of 6G
networks [112]. Currently, 5G networks only integrate partial AI and ML
techniques while it is expected that in 6G networks AI and ML will be fully
integrated with 6G networks like the concept of Industry 5.0.

####  IV-C1 Unsupervised and Reinforcement Learning

Unsupervised and reinforcement learning techniques could be used as a
potential learning technique in 6G networks. In 6G networks, a massive amount
of data will generate and it will not be an easy task to assign labels as
required in supervised learning; whereas, unsupervised learning does not
require such labeling and is capable of representing a learning model for such
data. Furthermore, unsupervised learning in a combination with reinforcement
learning could produce efficient autonomous systems [68].

TABLE V:  Network features of 4G, 5G and 6G  |  4G |  5G |  6G
---|---|---|---
Usage Scenarios |  • MBB |  • eMBB • URLLC • mMTC |  • umMTC • ERLLC • ELPCs • LDHMC • FeMBB
Applications |  • High-Definition Videos • Voice • Mobile TV • Mobile Internet • Mobile Pay |  • VR/AR/360° Videos • UHD Videos • V2X • IoT • Smart City • Telemedicine • Wearable Devices |  • umMTC • Holographic Verticals and Society, Tactile/Haptic Internet • Full-Sensory Digital Sensing and Reality, Fully Automated Driving • Industrial Internet, Space Travel • Deep-Sea Sightseeing • Internet of Bio-Nano-Things
Network Characteristics |  • Flat and All-IP based |  • Intelligence integration • Clouds architecture • Software virtualization • Slicing |  • Cloudization • Softwarization • Virtualization • Slicing
Technologies |  • OFDM • MIMO • Turbo Code • Carrier Aggregation • Hetnet • ICIC • D2D Communications • Unlicensed Spectrum |  • mm-wave Communications • Massive MIMO • LDPC and Polar Codes • Flexible Frame Structure • Ultradense Networks • NOMA • Cloud Computing • Fog/Edge Computing • SDN/NFV |  • THz Communications • SM-MIMO • LIS and HBF • OAM Multiplexing • Laser and VLC • Blockchain-Based Spectrum Sharing • Quantum Communications and Computing • AI/Machine Learning

###  IV-D Summary

Table V provides a comparison of 4G, 5G, and 6G technologies in terms of the
usage scenarios, applications, network characteristics, and technologies. In
4G networks, a simple implementation of MBB was available which evolved into
eMBB and provides better mobile broadband. 5G networks also support eMBB,
mMTC, and URLLC and it is expected that these technologies with further
enhanced like umMTC and ERLLC will support enhanced low latency and massive
machine to machine communication. The use of ERLLC, umMTC, and FeMBB 6G will
support innovative applications like Holographic verticals, Internet of bio-
Nano-Things and deep-sea sightseeing, etc. A huge shift in technologies can
also be seen among 4G, 5G, and 6G networks. 4G mainly used OFDM and MIMO; 5G
uses mm-wave and massive MIMO, and 6G will use THz band and SM-MIMO, etc. to
achieve its goal in terms of delay and throughput.

##  V AI-Enabled Technologies for 6G Wireless Networks

The rapid advancement of wireless networks will make 6G significantly distinct
from the earlier generations, as it will be exemplified by a high level of
heterogeneity in several aspects including network infrastructures, network
access mechanisms, dual-band devices, processing resources, etc. Furthermore,
the size of the massive data generated in wireless networks is rising
substantially. In this section, we promote AI as an essential tool to expedite
intelligent decision-making and learning for 6G wireless networks [59]. We
discuss and review different works which addressed these issues [16, 39, 15,
46, 73, 79, 59, 22, 21, 49, 50, 42, 34, 17].

###  V-A Big Data Analytics for 6G

For 6G wireless networks, there are four main categories of data analytics
that can be potentially applied. These categories include descriptive,
diagnostic, predictive, and prescriptive analytics. Descriptive analytics uses
historical data sets to obtain perceptions performance indicators including
traffic profiles, channel states, user viewpoints, etc. Descriptive analysis
helps to extend the familiarity of the network operators [71, 76, 92].
Diagnostic analytics is used for the identification of network failures with
the potential reason of failure or fault so that future systems can be
improved. Predictive analytics closely observes the data pattern in historical
data and predicts future behavior and predictions. Predictive analytics are
tied with machine learning techniques to get optimal results. Prescriptive
analytics helps to improve various domains in learning including resource
allocation, network virtualization, cache management, edge, fog computing, etc
[79].

###  V-B AI-Enabled Closed-Loop Optimization

Existing optimization mechanisms for wireless networks are considered to be
inappropriate for 6G wireless networks due to various characteristics of 6G
including dynamicity, complexity, the massive density of devices, and
heterogeneity. Furthermore, existing mathematical optimization techniques have
shortcomings for such massive 6G networks. For example, the objective
functions in the algebraic form help optimizers and make a simple solution;
however, 6G wireless networks have different scenarios, and these solutions
are not suitable. One of the potential AI mechanisms is an automated and
closed loop that can be used for 6G wireless networks. In this regard, current
AI mechanisms, like reinforcement and deep reinforcement learning (DRL) can be
used, which can establish a feedback loop between the decision process and the
wireless network system. By using this approach, the decision-making process
will make the best decision based on current situation [73].

###  V-C Self-healing in Wireless Networks

Advanced AI technologies could be useful to optimize the physical layer’s
performance of wireless networks. The existing wireless systems have various
physical layer issues including noise, channel and hardware impairments,
quadrature imbalance, interference, and fading. AI technologies can provide an
optimal way to communicate among different hardware. In the future, AI is
supposed to provide self-healing and self-optimizing mechanisms for sensor-
based 6G networks [90].

###  V-D Summary

It is expected that in 6G networks, a fully ML and AI-enabled network will be
in operation. That’s why it is very important to provide a detailed analysis
of upcoming network architectures with their requirement in terms of AI. Table
VI shows the link between various existing and future network architectures
and related AI algorithms. SDN, NFV, and Cloud/fog/edge computing are
considered potential network architectures that will use a fully AI-based
environment to support 6G networks. Currently, these architectures partially
support AI and ML aspects.

TABLE VI: Network Architectures and AI Algorithms  Network Functions |  Descriptions |  AI Algorithms
---|---|---
Software Defined Networks (SDN) |  In SDN, the control and data-forward function are decoupled to achieve programmable network management and configuration |  • DNN • Enhanced Q-Learning • Support Vector Machines • Self-Organizing Maps • Biological Danger Theory • Gradient-Boosted Regression • Deep Reinforcement Learning
Network functions virtualization (NFV) |  In NFV, hardware is decoupled from Software and reduce the dependency of network functions over hardware |  • Self-Organizing Maps • Biological Danger Theory • Gradient-Boosted Regression • Deep Reinforcement Learning
Cloud/Fog/Edge Computing |  Cloud computing represents a large pool of servers and systems which Provides data access and use networks of shared IT architecture. Fog computing extends the edge of the network and enhances the operation of computing, resources, and services between end devices and data centers over cloud architectures. Edge computing brings processing near to the data centers which improve the performance of data transmission. |  • Enhanced Q-Learning • Support Vector Machines • Self-Organizing Maps • Biological Danger Theory • Gradient-Boosted Regression • Deep Reinforcement Learning

##  VI 6G Potential Use Cases

6G network acts as a successor to the 5G cellular network. 6G networks will
operate on higher frequencies than the 5G networks to provide higher bandwidth
(in terabytes) and ultra-low latency (in microseconds). It is expected that 6G
technology will bring a huge performance improvement for various existing
communication technologies. For example, it is estimated that cellular data
will expand 3-fold from 2016 to 2021 and there will be more than 125 billion
connected cellular users globally by 2030 [30].

This section discusses the characteristics and requirements of some potential
use cases of 6G networks. There are many research works that discuss the
potential use cases for 6G technology [117, 121, 84, 67, 114, 120, 72, 20, 53,
31, 101, 98, 56, 13, 55, 91, 27, 118, 110, 10]. Based on the technology used,
we categorize the potential use cases into three broader categories: VR,
health, and Industry 5.0 as shown in Figure 2. These categories have various
sub-categories which are discussed in this section.

###  VI-A Virtual Reality (VR)

VR can be further divided into the following two categories:

####  VI-A1 Extended Reality (XR)

XR is a broader term that includes technologies to enhance our senses [24]. It
mainly includes augmented reality (AR), mixed reality (MR), and virtual
reality (VR). AR, MR, and VR over wireless links have been emerging as the
potential future applications targeting various areas such as education,
training, entertainment, tourism, sport, and gaming.

AR takes a real-world environment and adds a computer-based input to the
environment. Hence, AR is a collaborative activity where real-world
environment experience is obtained. The environment is built of enhanced
objects and these objects are available in a real-world environment. AR
technology makes use of the various sensors to experience the modalities like
visual, haptic, olfactory, motion, and somatosensory. AR consists of three
basic features: 3D visualization of objects, real-time collaboration, and a
mixture of virtual and real-world environments [25].

Figure 4 shows the AR-based user interface for the mobile applications of
IKEA, where customers can try various options according to their real-world
environment. For example, in a dulux-based AR application, a customer can
apply different colors in his real home environment, and in an IKEA
application, a customer can try various furniture in a real home environment.
Furthermore, these applications also handle the measurement and size issues
where you need to deploy the furniture.

Figure 4: IKEA AR-based applications for customers [17]

VR provides a purely virtual environment without considering user
surroundings. VR-based devices such as HTC Vive and Google Cardboard provide
users with a completely imaginary environment such as a squawking penguin
colony with dragons. MR combines the characteristics of VR and AR. MR
technology has started to take off with Microsoft’s HoloLens, one of the most
famous mixed reality apparatuses [115].

These applications process exceptional QoS challenges including quality of
experience, latency, and capacity/user. To achieve this, technologies like fog
and cloud computing will help to act as computing and intelligence layer for
AR/VR applications. 4G has been proven as a key enabling technology for data-
hungry applications like video-over-wireless. Later, the high use of these
multimedia applications was managed by 5G using the mmW spectrum. The mmW
spectrum attracted more data-intensive applications like AR, VR, and MR. It is
expected that in the coming years, the 5G spectrum will be depleted due to the
increasing use of these data-intensive applications as they demand system
capacity up to 1 Tbps [47]. One of the reasons for high system capacity is
that the data used in AR/VR/MR-based applications cannot be compressed as it
is presenting real-time interaction. Furthermore, managing real-time user
interaction with AR/VR/MR-based data-intensive applications requires a delay
in micro-second.

####  VI-A2 Holographic Telepresence

Holographic telepresence is a growing technology for fully interactive 3D
video conferencing. Holographic telepresence systems are capable to present
real-time, full-motion-based 3D images of people and objects in a room. It
also accommodates real-time audio and associates it with a particular 3D image
of a person. Images of people and surrounding objects at a remote location are
initially captured, then compressed and transmitted over a broadband network.
At the destination, the data is decompressed and similarly projected through
laser beams as a conventional hologram is produced. Holographic telepresence
is capable of revolutionizing various types of communications. For example,
telepresence in teleoperation can allow medical specialists to engage in real-
time complex operations from thousands of miles away. Furthermore, this
technology can also reduce the travel requirement for personal and business
meetings. Distance learning using telepresence can shape the future of
universities and colleges. Other prominent examples include programmed movies,
entertaining games, 3D navigation applications, etc. The applications
mentioned above like a 3D holographic display require a set of high-level QoS
requirements. According to a study, [17], a raw uncompressed hologram having
colors, and parallax, with 30 fps requires a data rate of up to 4.32 Tbps with
a strictly bounded delay of a few milliseconds. 3D holographic requires such a
high data rate due to the requirement of thousands of synchronized angles. 6G
network is capable of providing such high throughput and less delay required
by these applications. Furthermore, an acceptable visual quality at the remote
site is a challenging task.

###  VI-B Smart Healthcare

The key healthcare challenges for the world’s population include the
increasing rate of the elderly population, the increase in healthcare cost,
and the high death rate because of chronic diseases [2, 19]. In counties like
the United States, Germany, United Kingdom, France, Switzerland, Japan, and
Australia, the life expectancy has inclined to 79.3 years, 81 years, 81.2
years, 82.4 years, 83.4 years, 83.7 years, and, 82.8 years respectively. It is
expected that a high growth rate will overload the performance of existing
healthcare systems and the healthcare cost will also increase. Furthermore, a
large number of the population dies from fatal and chronic diseases like blood
pressure, cardiovascular, diabetes, and asthma. Research efforts revealed that
the effect of these diseases can be controlled if they could be identified in
the initial stage. To achieve this, future healthcare systems should be
revolutionized using digital and communication technologies so that proactive
wellness could be provided through early detection and monitoring services. It
is estimated that the need for remote healthcare monitoring services will
reach 761 million in 2025 [66, 122].

Figure 5: Remote patient monitoring in a hospital

These services will extend from monitoring to telemedicine solutions like
remote real-time surgery etc. The existing 5G spectrum has started its role in
remote healthcare; however, the use cases like remote surgeries, intensive
health monitoring in an elderly care center where each person is equipped with
enormous biomedical sensors, and remote healthcare for thousands of soldiers
in battle-fields require ubiquitous health monitoring with high data rates in
Gbps and Tbps, and ultra-low throughput to accommodate real-time solutions. In
this context, 6G networks are capable to revolutionize remote healthcare by
reducing time and space obstructions [121]. 6G’s technological enhancements
will showcase the eHealth applications using technological revolutions like
mobile edge computing, AI, fog, and cloud computing.

Figure 5 shows a typical remote patient monitoring system for hospitals where
patients, nurses, and medical devices are equipped with various healthcare
sensors, and the monitoring data is sent to the Internet and cloud computing
architectures for further processing. The doctors can see the different
results online and can monitor and diagnose the patients in real-time from any
remote location.

####  VI-B1 Brain–Computer Interaction

A brain-computer interface (BCI) is a communication pathway between a sensor-
equipped and an external device. The main objectives of BCIs include repairing
human cognitive or sensory-motor functions [107, 53]. The sensors collect the
brain signals and send them to a digital device for analysis.

Research on BCIs started at the University of California, Los Angeles (UCLA)
in the early 1970s, under research funding from the National Science
Foundation. Recent studies in the area of Human-Computer Interaction (HCI)
show that machine learning algorithms can play a key role in successfully
extracting features from the frontal lobe and then classifying mental states
(Normal, Neutral, focused) and emotional states (Good/bad mood) using EEG
generated brain-data [107, 53]. Such communication requires ultra-reliable
communication with very low delay, values in microseconds. The characteristics
of 6G architecture will support the deployment of BCI systems.

####  VI-B2 Human Senses-Based Applications

Humans have five basic senses: hearing, vision, taste, smell, and touch. In
the future, applications will be available that will be capable of
transferring human senses data to a remote location for processing. Such
applications use sensor-based technologies which make use of neurological
methods. It requires ultra-low delay and high throughput. The 6G networks can
provide such services [30, 95].

###  VI-C Industry 5.0

Industry 5.0 or the Fourth Industrial Revolution, is the recent revolution of
traditional manufacturing using smart technology. Technically, it mainly
emphasizes the usage of large-scale machine-to-machine communication (M2M)
with the help of Internet of Things (IoT) architectures and deployments to
obtain automation [98]. Industry 5.0 will make the machine’s components
capable to perform self-diagnostic and predicting any possible future issues
using AI algorithms so that any future failure could be avoided. Such
decisions constitute the first steps of the Industry 5.0 era, where machines
will make autonomous decisions and these decisions will have significant
effects on people’s lives. High achievements obtained in many areas with AI
algorithms are the basis of these developments in the industry.

Furthermore, machine components should be capable of communicating with the
other components and other machines. Predictive maintenance, IoTs, smart
sensors, and 3D printers are the main key enablers for Industry 5.0. 6G
technology will further promote the Industry 5.0 concept which was initiated
with 5G. Industry 5.0 introduced the idea of the digital transformation of
manufacturing through cyber-physical systems (CPS), which opens the gates
between physical factories and virtual computation. Such a combination will
promote new emerging technologies like M2M communications in a cost-effective
way [98]. Efficient automation systems in terms of asynchronous communication
are considered key enablers of Industry 5.0, in this regard, 6G is proposing
the use of terahertz domains to provide the required QoS for smart integrated
systems. Figure 6 shows the evolution of Industry 5.0.

Figure 6: Automation under Industry 5.0 [51]

####  VI-C1 Indoor Coverage

The 5G infrastructure usually operates under the milimeterWave spectrum (60
GHz) for indoor scenarios; however, it faces issues like penetration in solid
materials. 5G also proposed the use of distributed antenna systems (DASs) for
indoor environments which poses issues like scalability and deployment. 6G
focuses on cost-efficient solutions for indoor communication scenarios using
ultra-high capacity wireless relays with the visible light spectrum [84].

####  VI-C2 Smart City

A smart city can be defined as an urban area equipped with various types of
sensors that collect data and then perform analysis of the collected data to
manage and improve the infrastructure, services, and quality of life. The
collected data may belong to various resources including citizens, installed
devices, and buildings. The embedded data analytical capabilities help to
manage traffic systems, power plants, schools, libraries, hospitals, water
supply networks, waste handling, crime detection, etc [62].

5G technologies act as a key enabler for a smart city; however, 5G only made
the city partially smart which means that inside a city, only the fragments
(traffic management, water management, electricity, etc.) are individually
smart but not holistically as a smart city. 6G technologies have the potential
to speed up the adoption of smart cities by offering required QoS using
innovative and disruptive communication technologies [120].

6G will provide support for user-centric M2M transmissions to further improve
the deployment of smart cities. Furthermore, to provide a long battery life
for the devices in smart cities, energy harvesting approaches will be used,
though, 5G initially promised to adopt energy harvesting solutions but still,
it is not being adopted.

####  VI-C3 Unmanned Vehicular Mobility

Unmanned vehicular mobility refers to vehicular mobility without a
driver/pilot on board. These vehicles can be controlled remotely and they are
capable of operating and navigating autonomously by sensing their environment.
There are different types of unmanned vehicles including Unmanned ground
vehicles (UGV) like cars and combat vehicles, Unmanned aerial vehicles (UAV)
such as drones and autonomous spaceport drone ships, and unmanned underwater
vehicles (UUV) and driver-less trains.

The locomotive industry is precipitously advancing towards fully autonomous
transport systems which will ease the management burden on the organization
[72]. Connected and autonomous vehicles (CAVs) are one of the potential use
cases for future unmanned mobility. However, its deployment is still
challenging in terms of time-bounded services and high mobility (up to 1000
Km/h). Further, it is estimated that vehicles will be fitted out with a high
number of sensors (greater than 200 per vehicle by 2020) which can be only
served with terahertz spectrum [20]. Figure 7 shows a self-driving car example
in Australia with all embedded features and requirements. Moreover, flying
vehicles like drones indicate a massive potential for several use cases:
industrial construction, agriculture sector, and entertainment. 6G can provide
services for such massive data-hungry use cases.

Figure 7: An unmanned car with numerous sensors [60]

####  VI-C4 Internet of Everything

The term IoE evolved a few years ago, and there is a clear difference between
the definition of IoE and IoT. IoE is defined as intelligent connectivity
among things, data, processes, and people. The data is converted into useful
information and delivered to the right person/object at right time. Billions
of objects update their status by using various sensors. The IoT can be
defined as the network of physical objects that are accessed on the Internet.
IoE incorporates a massive network of things where each thing-network is an
autonomous system and capable of coordination with other networks [31]. IoT
and IoE have the same purpose in terms of connectivity; however, IoE’s
terminology is used for a broader aspect with massive data. IoE also
introduces the intelligence layer for decision-making. The IoE is expected to
provide massive connectivity in various domains including healthcare,
agriculture, logistics, manufacturing, and education. The 6G networks will
support such massive data connectivity using its flexible heterogeneous
architecture [101].

###  VI-D Summary

Section II provides a categorization of potential use cases for 6G networks,
the categorization includes VR, health, and Industry 5.0. VR is further sub-
divided into two categories including XR and teleportation. Both XR and
teleportation are discussed in detail with examples. The second important
category belongs to healthcare and a couple of potential use cases are
discussed under this category including BCI and human sense-based
applications. The discussion about the implementation of BCI for the 6G
network is provided. Industry 5.0 is discussed as the last category of the
potential use cases which maps the AI and ML with industrial applications.
Industry 5.0 is being considered a revolution for the automated industry and
it is vital to link it with upcoming 6G networks.

##  VII 6G Application Requirements and Challenges

Recently, a variety of AI-based mobile applications emerged that require
stringent QoS communication services. Many works have tried to address these
requirements [114, 105, 23, 97, 75, 14, 80, 26, 111, 82, 36]. This section
discusses how 6G will manage mobile AI applications.

###  VII-A Trends and Challenges

AI has performed well in many domains including computer vision and natural
language processing. AI processes are computationally rigorous and implemented
at data centers with custom-designed servers. With the advancement of mobile
communication and Internet of Things devices, numerous applications will be
operating in the future and are expected to implement at the edge of wireless
networks. The design of the 6G wireless network needs to consider the
hardware, QoS, and software requirements for IoT-based applications. Wireless
networks in the 6G-AI context face various challenges including privacy, data
storage, resource limitations on edge devices, managing heterogeneous devices,
and performance QoS [79]. The AI techniques like federated learning, deep
learning, and neural learning have the potential to cater to the challenges of
6G wireless networks [73].

###  VII-B Security and Privacy

Security and privacy are the key issues in 6G, and it is very important to
understand security and privacy concerning various enabling technologies which
we already discussed in the previous sections. The security and privacy issues
include access control, malicious activity detection, authentication,
encryption, etc. In the 6G network, IoT will be one of the dominating areas
with its applications in healthcare (sensing devices) and industrial
automation, etc., therefore, it is important to address the recent privacy and
security issues otherwise 6G will not be able to get its performance boom. In
Table VII, we map these issues with enabling technologies. These issues are
discussed according to the technologies including Terahertz, AI, and
blockchain. For Terahertz communications, electromagnetic signatures are
proposed to address the issue of authentication. In [74], the authors proposed
a mechanism to identify the malicious activity in Terahertz. In [71, 76, 92,
48, 82], the authors proposed an access control mechanism, malicious activity
detection, authentication, and encryption. The authors in [63, 65, 38]
provided security solutions for blockchain in terms of authentication, access
control, and communication. All these proposed mechanisms apply to 6G
networks.

###  VII-C Communication for Distributed Machine Learning

Along with AI techniques, high-level distributed machine learning techniques
are required to make a successful execution of wireless networks in a 6G
environment. The machine learning techniques elevate the complexities of the
cloud wireless architecture, decision making at the network edge, and end
devices.

####  VII-C1 Communication-Efficient Distributed Training

The expanding processing and storage capacity of advanced communication make
devices capable of on-device training, learning, and processing of received
data. Yet, transmitting over the unpredictable wireless channel turns into a
substantial performance bottleneck for the mobile distributed networks. To
handle the security and privacy issues, federated learning [59] keeps the
training data at each device. Federated learning is an emerging distributed
ML/AI approach with privacy preservation, is specifically useful for various
wireless applications, and is used as one of the key solutions to obtain
ubiquitous AI in 6G.

TABLE VII:  Security and privacy issues in 6G-enabling technologies  Enabling Technology |  Paper |  Security Issues |  Proposed Solution
---|---|---|---
Terahertz |  [8] |  Authentication |  Electromagnetic signatures are proposed
Terahertz |  [74] |  Malicious activity |  The authors highlighted that signal is transmitted via a narrow beam, it can still be intercepted by an eavesdropper
Artificial Intelligence |  [71] |  Access control |  Authors proposed fine grained control processes
Artificial Intelligence |  [76] |  Malicious activity |  Proposed anomaly detection system
Artificial Intelligence |  [92] |  Authentication |  Unsupervised learning process is used as solution
Artificial Intelligence |  [48] |  Communication |  Antenna based communication is mapped with AI algorithm as a secure solution
Artificial Intelligence |  [82] |  Encryption |  ML and quantum based encryption schemes
Blockchain |  [63] |  Authentication |  A novel architecture is proposed for authentication
Blockchain |  [65] |  Access control |  Proposed a method that improves access control
Blockchain |  [38] |  communication |  Proposed to use hashing mechanism to validate transactions

####  VII-C2 Communication-Efficient Distributed Inference

In 6G networks, it is expected that AI services will be implemented at data
centers and end IoT devices, e.g., unmanned vehicles and drones. A lot of
research efforts have been made to design inference processes with very low
latency and cost. To handle the issues like massive computation, processing,
power, and privacy in 6G networks, mobile edge computing is considered as a
potential candidate [73]. For instance, in the case of a deep neural network,
at end devices, the features can be extracted for decision making, and later
this information can be communicated to the edge/cloud computed devices.
However, the heterogeneous nature of devices in 6G networks makes computing
and inter-processing mechanisms more challenging for neural networks [79].

###  VII-D Summary

Table VIII shows the relation between various network layers and potential AI
algorithms for those layers. AI algorithms can play a critical role to improve
the performance of operational activities of the network model layers. AI
algorithms will somehow automate the functionality of these layers. For the
physical layer, K-Means, DNNs, CNNs, and CCNSs algorithms are suggested. These
algorithms can easily be mapped with physical layer modulating techniques like
OFDM and MIMO. To optimize the frame performance in terms of scheduling and
error detection, at the data link layer, Q-Learning, supervised learning, and
reinforcement learning is suggested. At the network layer, to increase the
performance for routing and mobility, reinforcement learning, supervised and
unsupervised learning, K-Mean and Q-Leaning are advised.

TABLE VIII: Communication layers and AI algorithms  Network Functions |  Descriptions |  AI Algorithms
---|---|---
Physical Layer |  Physical layer transmits the data on the wireless medium using different modulating schemes including channel coding, advanced modulation schemes like OFDM and MIMO etc. |  • K-Means • DNN • CNNs • CCNNs • Auto-encoder
Data Link Layer |  Data link layer is mainly responsible for the frame Performance issues i.e., scheduling, error identification and correction mechanisms, flow control mechanisms, synchronization, queuing etc., |  • DNNs • Q-Learning • Reinforcement Learning • Supervised Learning • Transfer Learning
Network Layer |  Network layer is responsible for connection management, mobility management, load and routing protocols management |  • DNNs • Reinforcement Learning • Supervised Learning • Unupervised Learning • K-Means • Q-Learning

##  VIII Future Research Directions

Numerous technical issues need to be resolved for the successful deployment of
6G networks. Some of the important concerns are discussed below.

###  VIII-A High-Level Propagation and Atmospheric Absorption of THz

The THz frequency bands offer very high data rates. Yet, the THz bands require
to resolve an important issue regarding data transmission over quite long
distances which is high propagation loss due to atmospheric absorption
characteristics, therefore, absorptive effects are witnessed [77]. The
atmospheric situation is often variable and as a result very unpredictable. To
solve this issue, a new transceiver architecture with efficient antennas for
the THz frequency bands is required. Furthermore, health and safety issues
related to the THz band need to be resolved.

###  VIII-B Complexity in Resource Management for 3D Networking

3D networking is a capability where someone can create a stunning 3D network
diagram for a better understanding of the network. 3D networking is considered
one of the powerful future networking aspects that will merge with the 6G
network. The 3D-based networks expanded in the vertical direction and it
requires innovative methods for resource management, routing protocol,
mobility support, and channel access. Huge attention is required from industry
and academia to implement the concept. It will merge with 6G networks and
provide better networking operations and management [52].

###  VIII-C Heterogeneous Hardware Constraints

6G network is envisioned as a massive collection of heterogeneous devices,
frequency bands, architectures, typologies, and operating systems.
Furthermore, hardware operational settings for the mobile terminals and the
signaling devices will be substantially different. The MIMO technique will be
more advanced in 6G with a further complex architecture than the 5G. Moreover,
6G will need to adjust to the complex communication protocols having
complicated algorithms. The addition of advanced AI and machine learning
techniques like unsupervised and reinforcement learning could make hardware
operations more complex. Subsequently, it will be very challenging to
incorporate all the heterogeneous aspects into a single platform [103].

###  VIII-D Autonomous Wireless Systems

Designing autonomous wireless systems with simultaneous responses in real-time
is not an easy task and cannot be achieved by incremental changes to existing
control and optimization methodologies. It needs a fundamental shift in the
system by merging machine intelligence in wireless architecture and
infrastructure. Autonomous wireless systems in 6G networks will be a
combination of several heterogeneous technological components like computing,
machine learning, autonomous cloud computing systems, and heterogeneous
wireless systems [34]. All the mentioned components require a specified
quality of service parameters to make a fully autonomous system, these
parameters include ML capabilities, quick data processing, huge storage
capability, hi Internet availability, and security services. With the advanced
infrastructure and features of the 6G networks, it is expected that it will
provide fully autonomous systems for various machines including cars, UAVs,
drones, etc. UAVs, are emerging as one of the potential 6G applications as
they can provide ultra-high data rates and ubiquitous wireless connectivity.
UAVs’ special features such as ease of deployment, high LoS link probability,
and a large degree of freedom are given through their controlled mobility.
UAVs can be deployed to extend wireless connectivity in case of disasters such
as natural disasters etc. Allowing UAVs to adaptively reposition according to
environmental changes and users’ demands is one of the most valuable UAV
features. The objective of connecting the independent components to be joined
by the 6G will be made possible by aerial BSs due to simple deployment.

###  VIII-E Device Capability

The start-up of the THz spectrum will deliver new sensing capabilities to the
6G devices, these capabilities include high-definition imaging and frequency
spectroscopy, etc. In sequence with high precision, these augmented sensing
capabilities are subservient in understanding the context and could help to
build a trusted and secure environment. The 6G system requires more
capabilities from devices to efficiently operate. For example, advanced
smartphones equipped with AI, XR, and integrated sensing, should be
resourceful to enable 6G’s new features like a throughput of 1 Tb/s. The
existing 5G devices may have shortcomings to support some of the 6G features.
A massive number of devices connected in 5G should be compatible with 6G.

###  VIII-F High Capacity Backhaul Network Connectivity

One significant element in the connectivity solution is an efficient backhaul
connection. An appropriate backhaul design could be very challenging for
remote areas. High-level density access networks for 6G networks are very
distinct and pervasive. These access networks will be capable of providing
ultra-high data rates. In the future, the backhaul network will have a
critical role in providing massive connectivity between core and access
networks. Evolving satellite constellations such as Starlink by SpaceX and the
systems by OneWeb and Telesat have a lot of potential to support such
scenarios; however, these are expensive solutions. The optical fiber and FSO
networks are the more appropriate options and in 6G networks they have the
potential to further improve the performance of back-haul networks, hence
advancement is required for the back-haul networks [96].

###  VIII-G Spectrum and Interference Management

The shortage of the spectrum at higher frequencies and their interference
problems require efficient and innovative spectrum sharing schemes. Effective
spectrum management schemes are vital to achieving optimal resource
utilization with QoS maximization. For efficient deployment of 6G, researchers
need to tackle concerns like spectrum management for the heterogeneous
networks and devices under the same frequency band [78].

###  VIII-H Beam Management in THz Communications

Beamforming via advanced MIMO systems is a potential technology to support
high data rates. Though, beam management for high-frequency bands like the THz
band is a challenging task due to the propagation characteristics. Therefore,
the design of an effective beam management scheme to handle the propagation
characteristics for advanced MIMO will be a challenging task. However, for
seamless handover, it is also important to choose the optimal beam efficiently
in high-speed vehicular systems [77].

##  IX Conclusion

In this survey, we initially evaluated the key successes and challenges of 1G
to 5G, and then focused on the potential applications, enabling technologies,
specifications, and requirements, and AI-based wireless applications and
technologies of 6G. The 5G communication system is expected to be fully
deployed globally in 2022. However, the current 5G technologies are not
capable of catering to the increasing demand for the future wireless
communication which is envisioned for 2030.

Research activities on 6G technologies and systems are yet in their early
stages. This survey envisions the options and methods to accomplish the
objectives of 6G wireless communication. In this paper, we also presented the
potential applications for 6G with key enabling technologies so that stringent
QoS requirements of future applications can be fulfilled. These requirements
are ultra-high throughput (1Tb/s to 20 Tb/s), extreme -low latency (tens of
microseconds), high reliability, ubiquitous network connectivity, and very low
power consumption. To support such QoS, advanced network architectures and
technologies like fog computing, cloud computing, software-defined networks,
optical wireless networks, network virtualization, and THz spectrum
communications are required. Moreover, future applications will generate
massive data, to handle such big data, advanced AI techniques and algorithms
are required. We summarized some of the potential AI methods for specific
network architectures. Along with explaining the vision and goal of 6G
communications, we have identified the numerous technologies that could be
used for 6G communication. We also discussed the probable challenges and
research directions to achieve the goals for 6G.

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