# 5G-Advanced Towards 6G: Past, Present, and Future

Wanshi Chen is with Qualcomm; Xingqin Lin is with Nvidia; Juho Lee is with
Samsung; Antti Toskala is with Nokia; Shu Sun is with Shanghai Jiao Tong
University; Carla Fabiana Chiasserini is with Politecnico di Torino; and
Lingjia Liu is with Virginia Tech. Wanshi Chen, Xingqin Lin, Juho Lee, Antti
Toskala, Shu Sun, Carla Fabiana Chiasserini, and Lingjia Liu

###### Abstract

Since the start of 5G work in 3GPP in early 2016, tremendous progress has been
made in both standardization and commercial deployments. 3GPP is now entering
the second phase of 5G standardization, known as 5G-Advanced, built on the 5G
baseline in 3GPP Releases 15, 16, and 17. 3GPP Release 18, the start of
5G-Advanced, includes a diverse set of features that cover both device and
network evolutions, providing balanced mobile broadband evolution and further
vertical domain expansion and accommodating both immediate and long-term
commercial needs. 5G-Advanced will significantly expand 5G capabilities,
address many new use cases, transform connectivity experiences, and serve as
an essential step in developing mobile communications towards 6G. This paper
provides a comprehensive overview of the 3GPP 5G-Advanced development,
introducing the prominent state-of-the-art technologies investigated in 3GPP
and identifying key evolution directions for future research and
standardization.

###### Index Terms:

5G, 5G-Advanced, 6G, MIMO, AI, ML, Full-Duplex, Green Networks, UAV, RIS, XR,
Positioning, NTN, IAB

##  I INTRODUCTION

The ever-increasing demands for mobile broadband and expansion to the so-
called vertical industries (e.g., automotive, satellite, internet of things)
continue driving evolution in standard bodies such as the 3rd generation
partnership project (3GPP), most recently evidenced by the successful
standardization of the fourth generation (4G) long-term evolution (LTE) and
the fifth generation (5G) new radio (NR). In particular, 5G NR has witnessed
three releases of standardization, providing a robust framework supporting a
comprehensive set of use cases over a wide range of spectrum which can be
deployed in various scenarios (indoor or outdoor, macro or small cells, etc.).

The next major step in the evolution of 5G towards the sixth generation (6G)
mobile networks is called 5G-Advanced, a new term approved by 3GPP in April
2021 as a response to a new era of 5G. Built on the strong 5G foundation,
5G-Advanced will introduce numerous new capabilities to boost the performance
and to enable or expand new use cases and verticals to use 5G technology. At
the same time, several forward-looking topics as part of 5G-Advanced will also
build the bridge towards 6G technology development.

###  I-A The Path to 5G

When the standardization for LTE began in 3GPP in 2005, the framework for LTE
was designed to optimize data throughput and voice over internet protocol
(VoIP) capacity in macro deployments. Over the course of the subsequent and
continuing LTE evolution, additional use cases and scenarios were gradually
introduced [1], including the support of broadcast services, the extension to
heterogeneous networks, the support of device-to-device (D2D) and vehicle-to-
everything (V2X) communications, the expansion into unlicensed and shared
spectrum, and the addition of machine type communications (MTC) in two flavors
targeting different deployments and use cases: enhanced MTC (eMTC) and narrow-
band internet of things (NB-IoT). These additional use cases and scenarios not
only enriched LTE’s offerings, but also provided a valuable learning
experience for future generations of standardization, where the
standardization has to consider at the very beginning a variety of different
use cases, spectrum types, and deployment scenarios.

The first release of 5G, starting with Release 15, introduced a solid baseline
for rolling out 5G, first with non-standalone (NSA) networks together with LTE
and more recently, standalone (SA) 5G networks with 5G core but without the
need of using LTE [2]. One important aspect of 5G standardization is the easy
migration from LTE to 5G in commercial deployments. At the physical layer, 5G
NR is designed to be compatible with LTE, e.g., by supporting also a one-
millisecond sub-frame duration and the 15 kHz sub-carrier tone-spacing. Fig. 1
illustrates the timeline for 5G standardization in 3GPP, starting with the
first 5G workshop in 2015, which identified three high-level use cases for 5G
[3]:

  1. 1.

Enhanced mobile broadband (eMBB),

  2. 2.

Massive MTC (mMTC), and

  3. 3.

Ultra-reliable and low latency communications (URLLC).

Figure 1: An illustration of 5G timeline in 3GPP.

Release 15 was designed to be unified, accommodating and flexible in
consideration of the different performance requirements (e.g., in terms of
throughput, reliability, latency, coverage, and capacity) of different use
cases. It is also scalable, easily adapting to different spectrum and service
requirements. In particular, the spectrum supported by 5G is classified into
two frequency ranges (FR): FR1 and FR2. FR1 covers 410 MHz to 7.125 GHz while
FR2 is further divided into FR2-1 and FR2-2, covering 24.25 GHz to 52.6 GHz
and 52.6 GHz to 71 GHz, respectively.

Releases 16 and 17 addressed the necessary enhancements as a continuation from
Release 15 as well as adding capabilities for various vertical segments. The
inclusive and future-compatible 5G framework facilitates its evolution in
supporting or expanding features enabling new or additional services and use
cases, as witnessed in Releases 16 and 17. In particular, these features
include [1]:

  * •

MTC support, particularly, the accommodation of eMTC/NB-IoT into 5G and the
introduction of reduced capability (RedCap) user equipment (UE) targeting new
types of devices (e.g., wearables, surveillance cameras, and industrial
sensors);

  * •

Unlicensed and shared spectrum (e.g., 5 GHz and lower 6 GHz bands);

  * •

Non-terrestrial networks (NTNs) primarily for satellite communications,
supporting both eMBB and MTC services;

  * •

NR sidelink, supporting V2X, public safety, and network controlled interactive
services;

  * •

5G broadcast and multicast;

  * •

URLLC and industrial internet of things (IIoT).

The solid start of 5G in Release 15 and the expansion of 5G into the above
vertical areas in Releases 16 and 17 provide a comprehensive set of
standardized features in 5G, equipping operators and vendors with a rich set
of enablers for successful commercial deployments. There was a lot of
attention on IIoT with work on URLLC as well as on private networks and
positioning, enabling the use of 5G with industrial automation. At the same
time, 3GPP standardization continues its work in boosting the radio
performance and improving key metrics such as UE power consumption, uplink
coverage or achievable system performance especially with 5G multiple-input
multiple-output (MIMO) evolution.

###  I-B 5G-Advanced towards 6G

3GPP is now entering the second phase of 5G standardization, known as
5G-Advanced, built on the 5G baseline in 3GPP Releases 15, 16, and 17.
5G-Advanced will further expand and extend the 5G capabilities and use cases
in many ways, starting with Release 18 to be ready in early 2024 and
continuing for further releases in Release 19 and beyond [4]. The key
5G-Advanced topics are introduced in this paper.

This paper also looks into future evolution beyond 5G-Advanced, i.e., 6G, and
discusses technologies that are expected to come next. Although 6G
standardization is not started yet, there have been a plethora of 6G
initiatives around the globe, driven by research interest, industry
expectations, and strategic government plans. In November 2019, China’s
Ministry of Science and Technology set up a working group called “China 6G
Wireless Technology Task Force” responsible for the national 6G research and
development and another working group consisting of government agencies to
promote the development of 6G technology. In the U.S., the alliance for
telecommunications industry solutions (ATIS) launched Next G Alliance in
October 2020 to advance North American leadership in 6G. Japan established the
Beyond 5G Promotion Consortium and Beyond 5G New Business Strategy Center in
December 2020 to promote beyond 5G/6G development in Japan. Europe has also
launched various 6G initiatives, notably the Hexa-X project launched in
January 2021, which aims to shape the European 6G vision and develop key 6G
technologies to enable the vision. In June 2021, South Korea established a 6G
implementation plan to lay the groundwork for 6G research and development,
which aims to push to launch commercial 6G services by around 2028.

This paper is an attempt to summarize and overview many of the exciting
developments in 5G-Advanced towards 6G. The rest of this paper is organized as
follows. Section II provides an overview of 5G-Advanced in more detail to
explain the Release 18 contents. Further evolution of MIMO is covered in
Section III, followed by positioning evolution in Section IV. Section V
discusses the topological evolution with elements like uncrewed aerial
vehicles (UAVs), NTNs and network-controlled repeater nodes. Section VI
describes the support for extended reality (XR) services, which includes
augmented reality (AR), virtual reality (VR), or mixed reality (MR) type of
services. The sidelink evolution is introduced in Section VII, while
artificial intelligence (AI) and machine learning (ML) for the NR air
interface and for the 5G radio access network (RAN) are covered in Section
VIII. The paper continues with the discussion of duplex evolution studies, and
green networks (aiming to reduce the energy consumed by both networks and UEs)
in Section IX and Section X, respectively. Section XI presents additional 6G
candidate technologies, followed by the concluding remarks in Section XII.

##  II 5G-ADVANCED: KEY TECHNOLOGIES

The first three 5G releases, as described in Section I-A, provide a
comprehensive package of features in a forward-compatible framework, enabling
successful 5G commercial deployments not only for the traditional eMBB
services but also expansion into the vertical domains. While it is still
imperative to continue evolving 5G in response to the ever-increasing
immediate commercial needs, it is also necessary to start exploring new areas
to further unleash the potentials of 5G. This is particularly important
considering the mounting interests and efforts of 6G in academia and various
fora and alliances, as discussed in Section I-B.

Release 18 will be the first release of 5G-Advanced. Therefore, how to
determine the set of features to be included in Release 18 requires meticulous
planning and careful discussion. To that end, a 3GPP workshop focusing on RAN-
related features was organized in June 2021, which attracted over 500
contributions from about 80 companies, organizations, and research entities
[5]. The workshop was primarily done via conference calls, with more than
1,200 registered participants.

The contributions to the workshop were coarsely classified into three
different categories [5]:

  * •

eMBB-driven evolution;

  * •

Non-eMBB driven evolution;

  * •

Cross-functionalities (or new areas) for both eMBB-driven and non-eMBB driven
evolution.

From these contributions, it can be observed that the proposed evolution
directions are generally balanced in terms of:

  * •

Mobile broadband evolution vs. further vertical domain expansion;

  * •

Immediate vs. longer term commercial needs;

  * •

Device evolution vs. network evolution.

These balanced evolution directions formed the basis for the subsequent
discussion, leading to the final approval of the set of features in December
2021 [6]. These features can be roughly categorized into three different
categories (eMBB/non-eMBB/new areas), as shown in Table I. Note that such
classification may be subjective and thus arguable. However, it should provide
a high-level insight of the overall evolution directions of 5G-Advanced.

TABLE I: 3GPP RAN Release-18 package and rough classification Category |  Feature
---|---
eMBB |  \- MIMO evolution (both downlink and uplink)
|  \- Further NR coverage enhancements
|  \- Enhancement of NR dynamic spectrum sharing (DSS)
|  \- NR network-controlled repeaters
|  \- Multi-carrier enhancements
|  \- NR mobility enhancements
|  \- Dual Tx/Rx multi-subscriber identity modules (MUSIM)
|  \- In-device co-existence (IDC) enhancements for NR and multi-radio dual-
connectivity (MR-DC)
|  \- Mobile terminated small data transmission (SDT) for NR
|  \- Mobile IAB
|  \- Further enhancement of data collection for self-organizing networks
(SON) and minimization of drive test (MDT) in NR and LTE/NR dual-connectivity
(EN-DC)
|  \- Enhancement on NR quality-of-experience (QoE) management and
optimization for diverse services
Non-eMBB |  \- NR sidelink relay enhancements
|  \- NR sidelink evolution
|  \- Expanded and improved NR positioning
|  \- Further NR RedCap UE complexity reduction
|  \- Radio enhancements for XR
|  \- NR NTN enhancements
|  \- IoT NTN enhancements
|  \- Enhancements of NR multicast and broadcast services
|  \- NR support for dedicated spectrum with less than 5 MHz bandwidth for FR1
|  \- NR support for UAV
New Areas |  \- Study on AI/ML for NR air interface
|  \- AI/ML for NG-RAN
|  \- Study on evolution of NR duplex operation
|  \- Network energy savings
|  \- Study on low-power wake-up signal/receiver
|  \- Study on enhancement for resiliency of gNB-central unit (gNB-CU)

In the sequel, we will dive into the details of some of the Release 18
features, including massive MIMO, positioning, topological aspects, XR,
sidelink, AI/ML, duplexing, and green networks. We will introduce these
features based on the efforts not only within, but also outside, 3GPP.

##  III MASSIVE MIMO EVOLUTION

In this section, we first introduce the massive MIMO evolution in 3GPP
standards and then discuss in detail multi-user (MU-MIMO) related aspects as
well as how AI/ML can be used for MIMO operation. After that, we describe
future evolution of massive MIMO towards 6G.

###  III-A Evolution of Massive MIMO in 3GPP Standards

5G NR was standardized assuming the use of two-dimensional array with a large
number of antenna elements, often called massive MIMO, so that beamforming and
spatial multiplexing can be performed in a very flexible manner utilizing both
horizontal and vertical directions as illustrated in Fig. 2.

Figure 2: Illustration of the concept of Massive MIMO.

This approach was first taken in the full dimension MIMO (FD-MIMO) in LTE-
Advanced Pro in Release 13 [7] and was exploited in the first version of 5G
standard in Release 15 [8].

By forming a narrow beam, the base station can concentrate its transmission
energy along a desired direction and can improve the strength of the signal
received from a desired direction. Utilizing a two-dimensional antenna array
makes it possible to shape beams in both horizontal and vertical directions,
resulting in improved capability of distinguishing the signals to and from
multiple devices. The massive MIMO in 5G is the essential technology for
coverage enhancement, improved single user throughput, and cell throughput
improvement through the aggressive use of multi-user multiplexing in spatial
domain (a.k.a., MU-MIMO).

In 5G, downlink (DL) beamforming and MU-MIMO require the base station to know
about the DL channel state, which can be provided from the channel state
information (CSI) report from the UE as well as the sounding reference signal
(SRS) transmitted by the UE utilizing the channel reciprocity in case of a
time division duplex (TDD) system. The UE can generate its CSI report by
monitoring the CSI reference signal (CSI-RS) transmitted by the base station
either per antenna element or per beam. The latter helps reduce the amount of
time-frequency resource for CSI-RS transmissions. Support of high-resolution
CSI feedback plays a crucial role for the MU-MIMO operation. For the purpose
of DL data reception, the demodulation reference signal (DMRS) is specified in
NR, for which the same precoding as the data is assumed.

For the high frequency band operation, 5G supports analog beamforming in
addition to digital precoding and beamforming. This helps reduce the
implementation complexity caused by the use of several hundreds of antenna
elements to realize the required coverage by overcoming the increased
propagation loss. The analog beamforming results in the limitation that all
simultaneous channels and signals formed by the same set of antenna elements
have to be in the same analog beam. To cover all directions with this
limitation, the so-called beam sweeping operation is supported. Flexible beam
management functionality is supported to form and manage beams, which is
essential for high frequency (e.g., millimeter wave (mmWave)) band systems.

After the first version of 5G standard in Release 15, 3GPP specified
enhancements on various aspects of MIMO functionalities in Release 16 and
Release 17 [9, 10]. Some representative examples are described below. First,
Release 16 specified enhancements for reduction of the CSI feedback overhead
via spatial and frequency domain compression. This makes it possible to
improve the cell throughput through MU-MIMO operation without causing too much
feedback overhead. Second, various enhancements for beam management were
specified. For example, Release 16 specified layer 1 (L1) signal-to-
interference-plus-noise ratio (SINR)-based beam measurement to facilitate
interference-aware beam selection. Measures were specified for reduction of
signalling overhead and latency, including L1/layer 2 (L2) signalling-based
joint indication of DL and uplink (UL) beams in Release 17, among others.
Third, while Release 15 mainly focused on single TRP-based operation,
operation of multiple transmit and receive points (TRPs) was enhanced in
Releases 16 and 17. In particular, Release 16 specified non-coherent joint
transmission (NCJT) from multiple TRPs to improve DL data rates and spectral
efficiency, while Release 17 introduced the enhancement on CSI report to
represent joint channels across multiple TRPs involved in NCJT operation.
Furthermore, Release 17 extended the multi-TRP operation to involve TRPs of
multiple cells and specified the support for beam management across multiple
TRPs.

The scope of NR MIMO evolution in Release 18 is defined in [11]. Release 18
gives an emphasis on enhancements for UL MIMO especially targeting non-
smartphone type devices such as fixed wireless access (FWA), vehicles, and
industrial devices. These include the use of 8 transmit antennas to support 4
or more streams per UE, and the simultaneous transmission from multiple panels
particularly for mmWave band. CSI reporting may be enhanced for high and
medium UE velocities that were not the main focus of the previous releases.
Continuing the enhancement on multi-TRP operation, the L1/L2-signaling based
joint indication of DL and UL beams specified in Release 17 will be extended
to the case of multiple TRPs. CSI acquisition may also be enhanced for
coherent joint transmission (CJT), a new target scenario in Release 18, with a
number of antennas distributed over multiple TRPs. To improve the performance
of MU-MIMO operation in both DL and UL, a larger number (up to 24) of
orthogonal DMRS ports may be specified for cyclic prefix (CP) orthogonal
frequency-division multiplexing (OFDM).

MU-MIMO is a critical operation mode for realizing the performance benefit of
the massive MIMO system and was first introduced for LTE-Advanced in 3GPP
Release 10 [12]. Scheduling and precoding of transmitted signals for multiple
UEs are fundamental operations of MU-MIMO and can be very challenging in the
massive MIMO system due to the underlying massive number of antennas at the
base station. Success of AI/ML in various areas motivates the research for its
utilization for MU-MIMO scheduling and precoding. In addition, AI/ML is also
being investigated to improve the performance of MIMO receiver operations such
as channel estimation and symbol detection. The following section introduces
the academic research results on MU-MIMO and the application of AI/ML to MIMO.

###  III-B MU-MIMO Operation and AI/ML-Enabled MIMO

Unlike other wireless networks, cellular networks need to strike a balance
between cell-average throughput and cell-edge throughput. Accordingly, MU-MIMO
scheduling becomes a central piece to achieve this objective. Many signal
processing and/or optimization-based scheduling algorithms were introduced in
the literature tailoring towards different performance metrics. For example,
maximum throughput scheduling has been introduced to maximize the sum
throughput across all the users in the network [13] without considering
fairness among users. To address this issue, round robin scheduling has been
introduced where users take turns to obtain the radio resources so that the
resources can be allocated fairly among all users. Since the round robin
scheudling does not consider the network throughput, this type of scheduler is
also referred to as blind equal throughput (BET) scheduler [14]. To cover both
network throughput and user fairness, the generalized proportional fair (GPF)
scheduler has been introduced [15]. For delay-sensitive real-time traffic, the
modified largest weighted delay first (MLWDF) scheduler [16] and the
exponential proportional fair (EXP) scheduler [17] have been introduced for
cellular networks. In [18], a multi-phase optimization-based scheduler for MU-
MIMO has been introduced by leveraging large-scale parallel computation to
speed up the scheduling speed.

Meanwhile, in cellular networks, the input information to the network
scheduler is usually the feedback information from the mobile users. Very
often this information is either coarse (due to the payload limitation and
granularity on channel feedback) or erroneous (due to feedback error). Since
conventional scheduling methods solely rely on the input information, their
scheduling decisions are far from being optimal especially when the input
information is not accurate.

Motivated by the success of AI/ML, the scheduling problem can be formulated as
an optimal control problem of Markov decision process (MDP), which can be
solved using deep reinforcement learning (DRL). In [19], a DRL-based
scheduling strategy has been introduced under the assumption that all resource
block groups (RBGs) are allocated to a single user in each transmission time
interval. In [20], a DRL-based scheduling strategy has been introduced to
adopt the same policy network to allocate every RBG so that the significant
training costs and convergence issues are avoided. In [21], a DRL-based
scheduler was introduced where the action is restricted to only determine the
number of RBGs allocated to each user. Even though this initial investigation
assumes certain idealistic assumptions, they did shed lights on applying ML
models to solve the challenging problem of MU-MIMO scheduling in an efficient
and resilient way. It is expected that the frameworks of DRL and/or multi-
agent DRL will play important roles to reduce computational complexity of MU-
MIMO scheduling.

MU-MIMO precoding is conducted once the scheduling decision is made. Despite
offering better performance, non-linear MU-MIMO precoding schemes, such as
dirty paper coding (DPC) or vector perturbation, are not practical for MIMO
systems due to their high implementation complexity. Simple linear processing
techniques have been shown to offer significant performance gains for MU-MIMO
systems especially for massive MIMO systems. Maximum ratio transmission (MRT)
and zero forcing (ZF)-based methods for MU-MIMO precoding [22, 23] have been
introduced. Low-complexity MU-MIMO precoding strategies that utilize the
direction of arrival (DoA) estimation have also been introduced for massive
FD-MIMO systems [24]. Similar to the MU-MIMO scheduling, AI/ML tools have also
been utilized in MU-MIMO precoding to alleviate the computational burden
especially for massive MIMO systems. For example, [25] introduces a deep
neural network architecture by unfolding a parallel gradient projection
algorithm to solve the weighted sum rate maximization problem for a multi-user
massive MIMO system. In [26], a graph neural network-based design has been
introduced to solve the joint beamforming and antenna selection problem with
imperfect CSI for a MU-MIMO system. In [27], a general form of iterative
algorithm-induced deep-unfolding neural network is developed in matrix form to
reduce the computational complexity for MU-MIMO precoding.

Other than being utilized in MU-MIMO scheduling and precoding, AI/ML tools
have also been recently applied to various other MIMO operations. For example,
various deep learning methods have been applied to the receiver processing of
MIMO operations such as channel estimation and symbol detection.

  * •

AI/ML for MIMO channel estimation: A convolutional neural network (CNN) is
adopted in [28] to learn the parameters of the minimum mean square error
(MMSE) channel estimator. Channel estimation was treated as a super-resolution
problem in [29, 30], where ChannelNet [29] and ReEsNet [30] have shown their
capability of substantially improving the channel estimation quality. However,
these works rely on offline training, where the artificially generated offline
training data is assumed to have the same statistical properties as the online
testing one, which cannot be guaranteed in a practical system. A reinforcement
learning-based successive denoising method is introduced in [31] to improve
the mean square error of least-square channel estimation. This method does not
need labelled data for training. However, it requires channel power and number
of channel taps as prior knowledge, and the training requires hundreds of OFDM
subframes to converge. StructNet introduced in [32] provides a real-time
online learning channel estimation method, which only requires over-the-air
pilot symbols for training and converges fast.

  * •

AI/ML for MIMO symbol detection: A deep learning framework, called DetNET, has
been introduced in [33] for MIMO symbol detection. In [34], a deep learning-
based symbol detector, MMNet, is constructed through unfolding existing
optimization-based symbol detection methods to deep neural networks. A special
recurrent neural network (RNN) with extremely low training complexity and
overhead, reservoir computing, has been introduced to MIMO-OFDM symbol
detection in [35, 36, 37]. Domain knowledge of the MIMO-OFDM waveform has been
incorporated in the design of underlying reservoir computing in [38] to
improve system performance without a substantial increase in complexity.
Furthermore, it enables a real-time online learning-based symbol detection for
MIMO-OFDM systems [39, 40]. In [41], a multi-mode reservoir computing has been
introduced to harness the tensor structure of the massive FD-MIMO channel to
boost the detection performance for multi-user massive MIMO networks.

###  III-C Future Evolution

There are active research activities for the evolution of massive MIMO
technologies towards 6G. This subsection briefly introduces the evolution of
massive MIMO for mmWave bands, modular massive MIMO, immense MIMO, and
holographic MIMO, among others.

####  III-C1 mmWave massive MIMO

The combination of mmWave and massive MIMO embodies the advantages of both
technologies, such as large bandwidth, high beamforming gain, and compact form
factor due to small wavelengths [42, 43, 44]. For a broadband mmWave signal,
the array pattern varies over frequencies, thus a large antenna array is
unable to generate beams pointing toward the same direction for a wide range
of subcarriers. This is known as beam squint [45, 46], which poses challenges
on transceiver design for mmWave massive MIMO systems. Three beamforming
architectures are generally considered for mmWave massive MIMO: analog
beamforming, digital beamforming, and hybrid analog/digital beamforming.
Analog beamforming, which usually relies on phased arrays, enjoys low hardware
cost and low implementation complexity, but it is unable to deal with the beam
squint effect since each phase shifter can only provide an identical phase
shift over different frequencies. Advanced codebook design has been proposed
to deal with this problem [45], but its benefit is limited when either the
transmission bandwidth or the number of antennas becomes large. True-time-
delay lines can compensate the delays of different antennas and naturally
produce frequency-dependent phase shift, but such a configuration increases
hardware complexity and limits the transmission rate. While beam squint can be
well handled by digital beamforming, the resultant hardware cost and
complexity are high. Low-resolution analog-to-digital converters (ADCs) can
significantly reduce cost and energy consumption, but it becomes difficult to
correctly decode the received symbols via spatial oversampling, thus decoding
with the beam squint effect under low-resolution ADCs remains an open problem.
For hybrid beamforming, despite various hybrid array architectures, each radio
frequency (RF) chain is connected to multiple antenna elements, thus the
corresponding drawbacks of analog beamforming still exist and necessitate
further study.

####  III-C2 Modular massive MIMO

Besides the high-frequency spectrum such as mmWave and THz bands, the low band
realm is still expected to play an essential role in future generations of
wireless communications to offer wide and reliable coverage. It can be
difficult to exploit massive MIMO in the low frequency band due to the half
wavelength spacing requirement between antenna elements. To address this
issue, architectures like distributed massive MIMO and cell-free massive MIMO
[47, 48], coordinated multipoint (also known as network MIMO), and distributed
antenna systems have been studied. Recently, a concept named modular massive
MIMO (mmMIMO) has been propounded [49], which consists of a variety of
predefined basic antenna modules that can be flexibly combined to build a
single system. Initial simulation results and field tests have shown the huge
potential of mmMIMO in improving throughput. Nevertheless, a few key issues
need to be solved to ensure the commercial success of mmMIMO, including the
effect of asynchronous reception among various antenna modules [50], DL and UL
reciprocity calibration, CSI acquisition and reporting, among others. In
addition, enormous work is needed to accurately quantify the benefits of
mmMIMO.

####  III-C3 Immense MIMO

There is increasing interest in utilizing arrays with extremely large number
(e.g., hundreds or even thousands) of antenna elements, exhibited in different
flavors including further evolution of 5G massive MIMO (denoted extreme MIMO
in [51]) and extremely large aperture array (ELAA). For convenience, in this
article, we group them together under the umbrella term immense MIMO. Extreme
MIMO is mainly targeted forcentimeter-wave (cmWave), mmWave, and (sub-)THz
range. For example, in case of a cmWave range such as 7-24 GHz, extreme MIMO
aims to improve the throughout performance while reusing 5G base station sites
[52]. The increased number of antenna elements poses technical challenges
including computational complexity, CSI feedback overhead, power consumption,
etc. In ELAA, better spatial resolution is achieved thanks to the increased
array aperture[53, 54]. Furthermore, the super spatial resolution is likely to
make the channels nearly orthogonal among different users, providing a
favorable propagation condition. Besides enhancing mobile broadband services,
the large spatial resolution of an ELAA can also be exploited for spatial
multiplexing of an unprecedented number of MTC devices. The growth of the
number of antennas results in an enlarged array size, which gives rise to new
phenomena including the near-field effect and spatial non-stationarity. While
there exists some work on the characterization of the near-field effect and
spatial non-stationarity for ELAA or similar architectures [55, 56, 57, 58],
more comprehensive studies and measurement campaigns are in need to thoroughly
explore the associated channel models and beam/wavefront management
strategies.

####  III-C4 Holographic MIMO

Another evolution trend of massive MIMO is holographic MIMO, sometimes also
named as large intelligent surface (LIS) in the literature, in which the
element spacing can be tinier than a half wavelength, and the entire array can
be regarded as a continuous electromagnetic aperture in its ultimate form [59,
60, 61]. A continuous aperture is able to create and detect electromagnetic
waves with arbitrary spatial frequencies (i.e., wavenumbers) without undesired
sidelobes, and offer fine controllability of the array radiation pattern. It
can also facilitate the sensing and imaging of RF signals [62], which is
promising in positioning along with integrated sensing and communication [63].
Furthermore, holographic MIMO enables highly efficient near-field power
transfer [59]. Currently, holographic MIMO can be implemented by either a
tightly coupled array of discrete active antennas [62], or low-profile
metamaterial/metasurface elements that do not require bulky electronic
components [64, 65, 66] and are excited by an RF source. The exploitation of
the full potentials of holographic MIMO requires accurate understanding of its
physical properties such as channel characteristics and beamforming
methodologies. While research work exists on some perspectives of the channel
modeling for holographic MIMO [60, 67, 68], more comprehensive channel models
and real-world measurements are in need to facilitate system design and
performance evaluation. Additionally, the theoretical performance limits of
holographic MIMO are worth investigating, which may necessitate new
communication theories, such as electromagnetic information theory, i.e., the
combination of information and electromagnetic theories [69, 70].

##  IV POSITIONING EVOLUTION

In this section, we first describe 5G positioning service requirements and
positioning architecture, then provide an overview of the 3GPP Release 17 NR
positioning methods and ongoing Release 18 NR positioning work, followed by
future outlook on positioning evolution.

###  IV-A 5G Positioning Services

5G positioning services aim to support verticals and applications with high
positioning accuracies [71]. Example verticals and applications that benefit
from high accuracy positioning include eHealth, factories, transportation,
logistics, mission critical services, and regulatory requirements. 5G system
can satisfy different levels of services and requirements (e.g., decimeter-
level positioning accuracy, 99.9% positioning service availability, and sub-
second positioning service latency [72]).

5G system supports the combination of 3GPP and non-3GPP positioning
technologies such as global navigation satellite system (GNSS), terrestrial
beacon system, sensors, and wireless local area network (WLAN) and Bluetooth-
based positioning. The service-based architecture of 5G core network used for
positioning services and corresponding network functions (NFs), NF services,
and procedures are specified in [73], while the UE positioning architecture,
functional entities, and operation to support positioning methods in next
generation RAN (NG-RAN) are defined in [74].

Figure 3: UE positioning architecture applicable to NG-RAN.

Fig. 3 shows the UE positioning architecture applicable to NG-RAN [74]. The
positioning information may be requested by and reported to a client within or
attached to the core network or in the UE. When an access and mobility
management function (AMF) initiates or receives a request for positioning
service for a target UE, the AMF sends a positioning service request to a
location management function (LMF) via the NL1 interface. The LMF processes
the position service request by, e.g., transferring assistance data to the
target UE and position estimation of the target UE. An LMF may have a
proprietary signaling connection to an evolved serving mobile location center
(E-SMLC) which may enable the LMF to access information from evolved universal
terrestrial radio access (E-UTRA). An LMF may also have a proprietary
signaling connection to a secure user plane location (SUPL) location platform
(SLP) which is responsible for positioning over user plane. A UE supporting
SUPL is known as SUPL enabled terminal (SET). The NG control plane interface
(NG-C) connects the AMF to an NG-RAN node (either a gNodeB (gNB) or a next-
generation eNodeB (ng-eNB)). The connection between the gNB and the ng-eNB is
via the Xn interface. The target UE is connected to the ng-eNB via the LTE-Uu
interface and to the gNB via the NR-Uu interface. The TRP in NG-RAN can be a
transmission point for transmitting downlink positioning reference signal
(PRS), a reception point for performing uplink SRS measurements, or a
combination of both a transmission point and a reception point. The LTE
positioning protocol (LPP), initially defined for LTE, has been extended to
support the positioning signaling between an LMF and a target UE in 5G
positioning [75, 76]. The positioning signaling between an LMF and an NG-RAN
node (gNB or ng-eNB) is transported by the NR positioning protocol-annex [77].

###  IV-B NR Positioning

Determining the position of a target UE involves two main steps: measurements
and position estimation based on the measurements. The position estimation is
usually computed with respect to network nodes with known positions. In UE-
assisted mode, the UE provides the measurements to a location server which
computes the position estimation. In UE-based mode, the UE computes the
position estimation based on the measurements. In network-based mode, the
network performs the measurements and computes the position estimation.

Positioning errors occur because the measurements are affected by noise,
interference, multipath effects, etc. NR provides improved positioning
accuracies by supporting larger signal bandwidths, denser network deployment,
and more antennas for transmission and reception [78]. Larger signal bandwidth
improves signal temporal resolution. Denser network deployment increases the
probability of line-of-sight (LOS) channel conditions between TRP and UE. More
antennas for transmission and reception improve signal directivity and thus
facilitate angular measurement based positioning methods.

The main NR positioning methods are summarized as follows [73].

  * •

Enhanced cell ID (E-CID): In cell ID based positioning, the position of the
target UE is estimated to be the position of the UE’s serving base station.
E-CID enhances the performance of cell ID based positioning by using
additional radio resource management (RRM) measurements such as reference
signal received power (RSRP) and reference signal received quality (RSRQ).

  * •

Downlink time difference of arrival (DL-TDOA) positioning is based on time-of-
arrival (TOA) measurements of DL PRSs from multiple TRPs received at the UE.
The TDOA values, referred to as DL reference signal time difference (DL-RSTD)
measurements, are calculated from the TOA measurements and can be used to
compute position estimation.

  * •

Uplink time difference of arrival (UL-TDOA) positioning is based on TOA
measurements of a UL signal from the UE received at multiple TRPs. Since the
UL TOA is measured at the TRPs relative to a common time reference, the TOAs
are referred to as relative TOA (RTOA) measurements. The RTOA measurements of
the TRPs are sent to a location server, which calculates TDOAs and uses the
TDOAs to compute position estimation.

  * •

Multi-round trip time (multi-RTT) positioning is based on two-way TOA
measurements, which are used to derive the UE Rx-Tx time difference and gNB
Rx-Tx time difference measurements. The RTT between the UE and the gNB can be
determined from the reported UE Rx-Tx time difference and gNB Rx-Tx time
difference measurements. Position estimation can be computed by using multiple
RTT values.

  * •

Downlink angle-of-departure (DL-AoD) positioning is based on per-beam RSRP
measurements of downlink PRSs from multiple TRPs received at the UE. The per-
beam RSRP measurements, combined with additional information such as the TRP
positions and DL PRS beam information (e.g., beam azimuth and elevation
angular information), can be used to estimate AoD values, which in turn can be
used to compute position estimation.

  * •

Uplink angle-of-arrival (UL-AoA) positioning is based on AoA measurements of a
UL signal from the UE received at multiple TRPs. The azimuth and zenith of AoA
measurements of the TRPs are sent to a location server, which computes
position estimation.

NR PRSs are fundamental for positioning measurements. A DL PRS resource can be
located anywhere in the frequency grid and has a configurable bandwidth with a
comb-like pattern in the frequency domain. The DL PRS resource may span
multiple consecutive symbols in a slot and have multiple repetitions within
one transmission period. The repetitions of the DL PRS resource are
transmitted with the same DL transmit beam which allows the UE to sweep its
receive beams over the repetitions of the DL PRS resource. A TRP can transmit
multiple DL PRS resources, each of which may be transmitted with a different
DL transmit beam. The UL PRS design is based on UL SRS with enhancements for
the positioning purpose, such as spatial relations and transmission power
control with respect to neighbor TRPs.

In Release 18, 3GPP continues NR positioning evolution [79]. The scope of the
work includes studying solutions for sidelink positioning, examining the
positioning support for RedCap devices, and investigating solutions to further
improve accuracy, integrity, and power efficiency for 5G positioning services.
The detailed solutions are now part of Release 18 [80].

Positioning is a valuable service that can find applications in diverse use
cases. As described in this section, 3GPP has been working on enhancing
positioning capabilities of 5G systems over several releases. Future new
services and applications will demand ultra-high accuracy positioning (e.g.,
below centimeter-level accuracy) within a few tens of millisecond latency,
which will exceed the positioning capabilities of the current 5G systems.
Besides positioning accuracy and latency, other metrics such as reliability,
availability, power consumption, scalability, security, and privacy will also
be essential design considerations for future positioning services. The
following section introduces the ongoing research on positioning, which might
become part of the 3GPP positioning work on the path to 6G.

###  IV-C Future Evolution

To meet the diverse extreme positioning requirements on the path to 6G, it is
vital to utilize a combination of positioning technologies and exploit
different signals such as satellite signals, communication signals, ultrasonic
sound, and visible light [81]. As an advanced technology, large antenna arrays
will become more prevalent across different frequencies towards 6G. They can
provide high spatial resolution, thus capable of achieving high positioning
accuracy. Reconfigurable intelligent surfaces (RISs) that reflect radio waves
in preferred directions can also be exploited for localization and mapping
[82]. LOS/non-LOS (NLOS) path identification in multipath propagation
environments is critical for geometry-based positioning methods. THz
technology with ultra-wide signal bandwidth and large antenna arrays can
achieve extremely fine time and spatial resolution, enable accurate LOS/NLOS
detection, and thus have the potential to improve positioning accuracy
significantly [83]. In addition, THz sensing can be used to construct 3D
images of the environment. It can enable simultaneous location and mapping
(SLAM) by combining high-resolution RF imaging with range and Doppler
information [84]. THz positioning techniques hold great potential for
supporting ultra-high accuracy positioning services on the path to 6G. Further
development of the current SLAM techniques towards exploiting THz localization
will be an essential evolution direction for improving positioning services.

Many geometry-based positioning methods rely on TOA or TDOA measurement of the
signals. Tight synchronization down to the sub-nanosecond level is vital for
these methods to achieve centimeter-level positioning accuracy [85]. Further
advancement of synchronization technology will be crucial to support ultra-
high accuracy positioning. Carrier phase positioning (CPP) is another
promising positioning technique that uses the phase-locked loop to measure the
phase information of received signal and derives the geometric distance
between transmitter and receiver from the measured phase. It has been used in
GNSS to achieve centimeter-level positioning accuracy [86]. However, it is
challenging for GNSS to offer centimeter-level positioning accuracy in dense
urban or indoor areas. Incorporating CPP into cellular positioning was studied
in [79] and CPP is being included together with other positioning enhancements
for 5G-Advanced as part of Release 18 [80]. CPP for cellular positioning will
also be an interesting evolution direction towards 6G [87].

Traditional geometry-based positioning methods have difficulty in achieving
high positioning accuracy in scenarios with heavy NLOS paths, such as indoor
factory environments. AI/ML-based positioning with fingerprinting or ray
tracing can overcome the challenge and has attracted much attention [88] [89].
Indeed, 3GPP studies AI/ML-based positioning as one of the use cases for
augmenting the 5G air interface with AI/ML in Release 18 [90]. However, to
make AI/ML-based positioning a success, several key aspects require further
investigation, such as data collection for AI/ML model training, performance
validation, and model generalization capability.

Last but not least, though data communication and positioning can coexist in
mobile networks, their integration has not been tight thus far. With the
emergence of integrated sensing and communication towards 6G (see Section XI-B
for more detail), data communication and positioning integration may become
tighter in 6G [91]. Joint design of communication and positioning to improve
both functionalities will be an important evolution direction on the path to
6G and beyond [92], which will be further discussed under the category of
joint communication and sensing (JCS) in Section XI-B.

##  V TOPOLOGICAL EVOLUTION

In this section, we introduce the major topological evolution in 3GPP
standards including integrated access and backhaul (IAB), repeaters, UAVs, and
NTNs. After that, we describe future topological evolution towards 6G, with a
focus on RIS.

###  V-A IAB Evolution

The support for the IAB node was introduced in Release 16 specifications
following the study as captured in [93]. IAB aims to provide an alternative
for fiber or microwave link based backhaul, by using 5G radio interface based
either in-band or out-of-band backhauling. Additional enhancements for IAB in
Release 17 [94] include improved robustness and load balancing, and reduced
service interruption by enhancing topology adaptation, routing, and transport.
Duplex enhancements (i.e., simultaneous transmission and/or reception of child
and parent links for an IAB node) and efficiency enhancements (e.g., power
control enhancements, extension to frequency/spatial domain multiplexing among
backhaul and access links) were also introduced.

Release 18 work on IAB [95] consists of enhancing IAB operation with mobile
IAB nodes. Such nodes would be assumed to be placed in a vehicle (e.g., in a
bus) providing coverage for UEs in the vehicle or in the close proximity of
the vehicle. The UEs have no visibility regarding whether the serving cell is
a mobile IAB node or not (i.e., vs. a regular gNB), although the service may
entail increased latency due to the extra hops in IAB for the connection.

###  V-B Repeater Evolution

Traditional RF repeaters [96] are simple but their usage in TDD bands may be
compromised, since these repeaters merely boost the received signals
irrespective of link directions. A “smarter” repeater, on the other hand, can
take into account the control information from a gNB, e.g., the TDD
configuration in use, such that a transmission may be omitted or appropriately
directed as desired, depending on the TDD configuration. These smart repeaters
are also known as network-controlled repeaters [97].

In Release 18, the support for network-controlled repeater would be done by
enabling necessary control information from the network towards the repeater.
The necessary information may include the UL/DL TDD configuration, repeater
ON/OFF configuration, and beamforming for improved repeater operation[97]. The
work is based on the outcome of the study in 3GPP as described in [98], with
the results captured in [99]. Controlling the repeater’s ON/OFF status is
important, e.g., for energy savings when the carrier frequency is turned off
for a period of time. There is also a possibility to control the gain and
power used by the repeater for more efficient network interference management.

###  V-C UAV in 5G

In Release 18, the work on connected UAV covers several key aspects [100,
101]. UAVs are supported to report to networks information such as flight path
or height for proper operation. UAV identification is important to help
control where the UAVs fly, which can be done by identifying UAVs which could
be causing problems (e.g., flying in areas not permitted for UAV use) to
ensure UAVs to fly only in authorized air space. 3GPP is also addressing
remote UAV identification broadcast to allow for meeting requirements expected
to be part of regulatory requirements in some regions. Besides the pure UAV
identification broadcast, considerations have also included potentially more
elaborated aspects of detecting other UAVs and avoiding them in order to
prevent collisions.

It is also critical to consider the impact of UAVs on the network, as UAVs
associated with heavy traffic (e.g., video feed) may easily create lots of
interference in the network. This is due to the fact that a UAV up in the air
may have LOS connections with many cells. With the use of 5G beamforming
capabilities, directive/beamforming antennas may replace omni-directional
transmission such that data flow from a UAV can be more directive, with this
aspect being studied also for Release 18. This is illustrated in Fig. 4.

Figure 4: Operation with different UAV antennas.

In such a case, the interference caused to the network can be heavily reduced.
In addition, UAV operation can be more reliable since UAVs would experience
less interference from different gNB transmitters. Further aspects of UAV
operation have been discussed, including optimization of handover procedures,
especially for conditional handover in connection with UAV connectivity. In
case that a UAV has a predetermined flight path (and known by the network),
one could expect more possibilities to prepare for handover in advance.

###  V-D NTN Evolution

Besides terrestrial topological evolutions, 3GPP has been working on non-
terrestrial topological evolution to support satellite communication.
Historically, the terrestrial and satellite communication systems have been
developed and evolved independently. Despite its high data rate and low
latency, the terrestrial mobile system covers about merely 20% of the land
area which is only 6% of the entire earth surface [102]. In contrast, the
satellite system can offer global coverage and higher survivability in case of
disasters such as earthquakes, but suffers long transmission distance hence
high latency. Therefore, 5G strives for the integration of terrestrial and
satellite communication systems, aiming at obtaining ubiquitous coverage and
high quality of service.

The most popular conventional satellite communication standard is the digital
video broadcasting – satellite – second generation (DVB-S2) standard and its
extension (DVB-S2X) from the European Technical Standards Institute (ETSI)
[103]. Meanwhile, 5G NR based satellite communication has been widely
discussed by standard organizations such as 3GPP and International
Telecommunication Union (ITU) [104]. Compared with 5G NR, the target spectral
efficiency is lower in DVB-S2X, and certain functions, such as uplink
synchronization, hybrid automatic repeat request (HARQ), control channel,
discontinuous reception (DRX), RRM measurement, mobility management and core
network, are in lack. Therefore, DVB-S2X is more suitable for fixed wireless
access rather than mobile communication service.

In 3GPP, NTN was first introduced in 5G NR Release 17 for a unified standard
for both the terrestrial communication and the satellite communication, the
details of which can be found in [105, 106, 107]. Compared with terrestrial
communication, satellite communication has its unique characters in terms of
propagation channel, propagation delay, satellite mobility, cell radius,
multi-layer coverage, and so on [108]. The 3GPP NTN work has been addressing
the following main technical challenges:

  * •

Challenges in transmission systems: In the satellite-ground integrated network
transmission, Doppler frequency shift, frequency management and interference,
power limitation, and timing advance are urgent problems to be solved. For
Doppler frequency shift, 5G adopts multi-carrier OFDM in the transmission
system, and its subcarrier spacing design does not consider the impact of
large Doppler frequency shift, which can bring interference between
subcarriers. In terms of power limitation, it is necessary to ensure high
frequency band utilization and reduce the signal peak-to-average ratio. With
regard to timing advance, the rapid change of wireless link transmission delay
may necessitate dynamic updates of the timing advance of each terminal to
ensure that all UL transmissions are synchronized.

  * •

Challenges in access and resource management: Taking LEO satellites as an
example, the RTT between an LEO satellite and an earth terminal ranges from
1.2 ms to 13.3 ms, while that for typical terrestrial communications is only
up to 1 ms. The long delay of the satellite-ground integrated network brings
challenges to the access control, HARQ, and other processes of medium access
control (MAC) and radio link control (RLC) layers. In terms of access control,
in order to support the effective integration of terrestrial and satellite
systems, it is necessary to design reasonable access mechanisms such as pre-
grant, semi-continuous scheduling, and grant free access. For HARQ, which has
strict requirements on time, the round-trip time usually exceeds the maximum
timer length of HARQ. In the scheduling process of MAC and RLC layers, the
long delay of the satellite system will also affect the timelines of
scheduling, thus its scheduling delay parameters need to be adjusted.

  * •

Challenges in mobility management: In the satellite-ground integrated network,
the challenge of mobility management is even severer. According to the
communication level, it can be divided into network-level handover and link-
level handover. According to the application scenarios, it can be divided into
inter-cell handover on the ground, handover between satellites and ground
cells, handover between satellite cells, and inter-satellite handover.

The main solution standardized in the 3GPP Release 17 to address the above
challenges is to enable the NTN network to broadcast satellite ephemeris-
related data. Furthermore, the NTN UE is assumed to be equipped with GNSS
capabilities. With ephemeris data and GNSS capabilities, the NTN UE can
calculate the relative speed and RTT between the UE and the satellite to
precompensate its UL frequency and transmission timing. UE can also utilize
the ephemeris data to predict the trajectory of the LEO satellites over time,
facilitating mobility management in NTN networks. We refer interested readers
to [104] for more details.

The latest Release-18 NTN objectives focus on the applicability of the
solutions developed by general NR coverage enhancement to NTN, particularly
for uplink channels [109]. Additional evolution of NTN in 3GPP is possible to
further user experience. In particular, at certain point in the future,
satellite communication may be integrated within 6G systems, deployed at high,
medium and low orbits, and working jointly with terrestrial communication. The
terrestrial network may be the core of the integrated system and controls the
entire space-based communication system.

###  V-E Future Evolution through RIS

We anticipate that 3GPP will continue topological evolution to embrace
emerging technology trends on the path to 6G. One potential major topological
evolution can be the inclusion of RIS. Broadly speaking, RIS is an umbrella
term for two-dimensional metamaterial-based arrays that can manipulate
electromagnetic waves via anomalous reflection, refraction, polarization
transformation, among other functionalities [110, 111]. Herein we focus on
RISs configured as anomalous reflective and/or refractive surfaces that can
customize the propagation environment by reflecting and/or refracting signals
to desired directions. Fig. 5 illustrates typical use cases for RISs.

Figure 5: Illustration of anticipated typical use cases of RISs.

Among the various types of RISs, reflective RIS is most common, which reflects
signals from the source node to the destination node while consuming little
energy, leading to high spectral efficiency and energy efficiency. The power
allocation, phase shift design, beamforming strategies, channel estimation
schemes, and a variety of other aspects regarding reflective RISs have been
widely studied in the literature [112, 113, 114, 115, 116]. Note that both
reflective RIS and relay can provide range extension. Compared with the
conventional relay, RIS has some distinguishing features such as hardware
complexity, power budget, noise, and average end-to-end SNR [117] as listed in
Table II.

TABLE II: Comparisons between RISs and relays |  Relay |  RIS
---|---|---
Hardware complexity |  A series of active electronic components |  Low-power and low-complexity electronic circuits (switches or varactors)
Power budget |  A large amount of active electronic components with high power consumption.
Total RF power is often allocated between the transmitter and the relay. |  Nearly passive implementation with low-power active components (switches or varactors).
Almost all RF power is allocated to the transmitter instead of between the
transmitter and the RIS.
Noise |  Additive noise or loop-back self-interference |  Phase noise
Average end-to-end SNR |  Proportional to the inverse of transmission distance |  Electrically large RIS: Proportional to the inverse of transmission distance.
Electrically small RIS: Proportional to the inverse of the square of
transmission distance [117]

Qualitatively, RIS can be regarded as a full-duplex MIMO relay without self-
interference and signal power amplification [118]. Overall, RIS-assisted
transmission may outperform relay-aided transmission in terms of data rate if
the size of the RIS is sufficiently large [117], but the performance gain may
be limited by the quantization of the phase shifts. Thus, it is still subject
to more comprehensive studies to determine whether RISs or relays/repeaters
are more suitable for commercial deployment, especially when considering
various factors including cost, use cases, and maintenance. To strive for
better performance and to take advantage of both techniques, some researchers
proposed combining relays with RISs and named this architecture as hybrid
relay-RIS (HR-RIS), in which a single or a few elements of RIS act as active
relays, while the remaining only reflect the incident signals. HR-RIS
potentially excels at spectral efficiency and energy efficiency in harsh
scenarios such as when the transmit power is low and/or when the HR-RIS is
located far away from the transmitter [119].

Another type of RIS deployment is to further consider its transmissive
properties to extend its application scenarios [120, 121], such as
simultaneous transmitting and reflecting RIS (STAR-RIS) [122], where part of
the incident signal is reflected into the space in front of the RIS and the
remaining part is refracted into the other side of the RIS. The substrate of
an STAR-RIS can be optically transparent such that it does not interfere
aesthetically or physically with the surrounding environment or people’s line
of sight. The power ratio of the reflected and penetrated signals is
determined by the hardware structure, and needs to be appropriately optimized
to enhance the performance over all users.

Besides theoretical analysis and simulations, early prototyping and testing
have also been conducted to demonstrate the performance gain brought by RISs,
including a recent multi-frequency field trial using off-the-shelf 5G UEs and
reference signals defined in 3GPP 5G standards [123] which showed 15 to 35 dB
RSRP enhancement by RISs.

Despite the great potential of RIS and some pioneering field trials, a number
of key challenges need to be handled in practical implementation, e.g.:

  * •

Inconsistent phase offset of an RIS across subcarriers for broadband
communications,

  * •

Channel estimation and feedback overhead for passive RISs,

  * •

Efficient power supply for RISs,

  * •

Performance degradation owing to element failure, and

  * •

Ratio of active and passive elements in an RIS considering the tradeoff among
channel estimation complexity, spectral efficiency, and energy efficiency.

Therefore, compared with its counterparts such as small cells and relays,
whether RIS can achieve the eventual commercial success requires further
investigation and more extensive trials.

##  VI XR EVOLUTION

In this section, we will start by describing XR-specific service requirements,
followed by several key considerations in serving XR in 3GPP. We will also
provide a brief overview of the efforts related to XR outside 3GPP.

###  VI-A XR-specific Service Requirements

XR is an umbrella term covering applications including AR, VR, or other forms
of expanded and immersive reality applications. The XR as a service differs
from the traditional mobile broadband services, as it demands low latency and
high data rates in line with the XR-codec periodicity. In particular, the
video codecs have often variable frame rate, which is not necessarily evenly
aligned with the frame structures used in 5G networks. A typical frame rate of
60 frames per second (fps) would result into a periodicity of approximately
16.67 ms, which naturally does not map well on the 10 ms 5G frame structure
with the transmission time internal of either 1 or 0.5 ms depending on the
frequency band and the resulting sub-carrier spacing in use for the sub-6 GHz
frequencies.

Besides the actual video transmission, it is necessary to enable relatively
frequent control or pose update signaling transmission, and to keep the
experienced delay below 10 ms, given that in some of the traffic models in use
[124] the control/pose update signalling is generated every 4 ms. Furthermore,
the XR applications will likely have different detailed timing
characteristics, and thus the envisioned solutions need to be configurable
enough to match them.

###  VI-B Key Considerations for the Support of XR in 3GPP

The first key step in supporting XR traffic efficiently is to identify such
traffic in the first place, which may be performed for example by extending
the 5G quality-of-service (QoS) flow identifiers to enable the detection of
the packets that are part of an XR service. Once XR packets are identified,
the scheduling functionality needs to be adapted to the timing characteristics
of a given XR service, such as the already mentioned frame rate as well as the
jitter that can be tolerated. Additionally, when dealing with services like
VR, there is also a trade-off between latency and the required maximum data
rate. When the connection has low enough latency, the system allows sending
accurately only content for covering the field of view, as it can react fast
enough for the change of gaze focus. A larger latency, instead, must be
compensated by sending also those parts of the view with high accuracy, which
would not necessarily be in the current field of view otherwise. Of course,
this also requires that the servers providing the service are not physically
located too far from the mobile user, in order to benefit from taking the gaze
focus into account, as shown in Fig. 6.

Figure 6: Illustration of XR service.

Another important factor in XR applications is UE’s power saving, as it is
desirable to use small and light devices for XR services. The use of
continuous high data rate suited for low latency causes the mobile devices to
consume too much power. Enabling the use of moments when XR data is not coming
for DRX operation and other reductions in the receiver processing pipeline
allows the UE to save power and, in turn, improve the usability of XR
services. Additionally, creating some breaks in the data stream is beneficial
from the network power consumption point of view as well.

To make the XR a real mobile service, the achievable mobility performance is
also important. While the reliability of the connection is fundamental, it is
also essential to minimize the interruption duration, as otherwise the
interruption in the data flow becomes visible in the end-user video stream or
the motion related control feedback experiences too much delay.

Many other improvements foreseen by 5G-Advanced will be of benefit to XR
services provision, especially improved uplink coverage and system capacity.
In addition to the radio interface aspects, edge computing-related
improvements have a direct relationship with XR, as XR application processing
in the network needs to be performed sufficiently close to the end user so as
to reduce total end-to-end latency.

3GPP has now established traffic model and evaluation assumptions for XR
evaluation over NR [125] and studied the potential improvements for XR
delivery over 5G-Advanced [126] and now XR improvements are being specified
for Release 18 [126]. Importantly, this makes delivery of XR services more
efficient and, thus, available to a larger number of simultaneous XR users
than in the first-phase 5G networks. As XR requires end-to-end handling, not
just in the radio access network but also in what concerns edge computing, a
significant deal of work addresses core network-related improvements,
especially looking at QoS evolution and application awareness to meet the
requirements of XR services [127]. In addition, related to the provision of XR
services, 3GPP is active in developing standards for appropriate codecs of
audio and video, as they will also help deliver XR services for a larger
number of users.

###  VI-C XR outside 3GPP

In recent years, XR has attracted a great deal of interest from both research
and commercialization perspectives. While the XR commercial market size is
already in the order of billions of US dollars, further significant market
growth is expected in the upcoming years with the development of advanced
commercial products.

XR devices may be tethered by cables or supported wirelessly. The former ones
facilitate satisfaction of the extreme requirements, but limit users’ mobility
and quality of experience. Wireless-connected XR devices instead can leverage
advanced wireless technologies to eliminate cables for more enjoyable users’
mobility anywhere and any time [128, 129].

While XR may be provided in LTE networks [130], 5G NR is expected to take the
VR/AR experience to the next level [131], thanks to extremely high throughput
(multi-Gbps), ultra-low latency (down to 1 ms), and uniform experience (even
at cell edge). The work [132] underscores the importance of VR technology as a
disruptive user case for 5G, and it presents a list of research avenues (e.g.,
caching, short-range wireless communications, context information and
analytics, computer vision and media, etc.) and scientific challenges (e.g.,
“Shannon-like” theory to maximize users’ immersive experience, quality-rate-
latency tradeoff, in-VR vs. in-network computation, scalability, privacy,
localization, and tracking accuracy). Furthermore, [133] lists a set of
technical enablers including mmWave communications, edge computing, proactive
computing and caching, multi-connectivity, and multicasting, while [134]
focuses on the reliability and latency achieved for VR services over a THz
cellular network. The study [135] covers resource management for a network of
VR users over small cell networks, where the problem is formulated as a non-
cooperative game from which a distributed algorithm is proposed, which
leverages ML to predict VR QoS for improved QoS utility.

##  VII SIDELINK EVOLUTION

In this section, we first introduce the sidelink evolution in 3GPP standards
and then discuss its safety applications. After that, we describe future
sidelink evolution towards 6G, with a focus on mesh networking.

###  VII-A Sidelink Evolution in 3GPP Standards

Before LTE Release 12, standardization of over-the-air transmissions in 3GPP
was primarily focusing on DL and UL Backhaul link in the relaying context was
standardized in Release 10 where a relaying node is assumed to be in-band and
half-duplex [136]. With the increasing need of D2D communications, 3GPP
started to study and specify D2D in Release 12 [137] and further evolved it
into V2X using the so-called sidelink, as illustrated in Fig. 7.

Figure 7: Illustration of various link types in 3GPP.

Fig. 8 illustrates the timeline of sidelink standardization in 3GPP. The
standardization started from Release 12 D2D communications, followed by LTE
V2X support from Release 14, and 5G NR V2X support from Release 16. The
standardization of V2X in LTE and 5G NR is often commonly referred to as
cellular V2X or C-V2X.

Figure 8: Sidelink standardization timeline in 3GPP.

The D2D standardization in Release 12 covers discovery, synchronization,
broadcast and groupcast communications [138]. Discovery is targeted towards
commercial applications, where a discovery signal can be periodically
transmitted with roughly 200 bits of information. Communications are for
public safety applications only, without any physical layer feedback such as
HARQ-acknowledgement (HARQ-ACK) or CSI. The physical layer structure for D2D
is completely compatible with LTE for smooth co-existence, where D2D may only
occupy LTE UL subframes. The resources for D2D can be either allocated by an
eNB, or randomly selected. D2D communications can be supported also for cases
when a UE is out of network coverage or partially in coverage.

Enhanced D2D operations were specified in LTE Release 13 for both discovery
and communications [139]. Discovery was expanded to accommodate inter-
frequency and inter-public land mobile network (PLMN) cases, and can be
performed with a transmission and reception gap. Out-of-coverage discovery is
supported as well. Enhancements to D2D communications include layer-3 UE-based
relay, priority handling, and multiple destination transmissions.

Starting from LTE Release 14, V2X is supported in 3GPP. The D2D interface
(also known as the PC5 interface) is not only used to support vehicle-to-
vehicle (V2V), but also for vehicle-to-infrastructure (V2I), vehicle-to-
pedestrian (V2P) and vehicle-to-network (V2N) for beyond safety and collision
avoidance [140]. The primary target spectrum is the unlicensed intelligent
transportation systems (ITS) spectrum [141]. During the standardization, the
maximum vehicle speed was assumed to be 250 km/h, implying a maximum relative
speed of 500 km/h. At a carrier frequency around 6 GHz, it would result in a
Doppler shift of about 2.7 kHz and consequently channel variations within a 1
ms subframe, bringing various design challenges. Wireless resource for V2X can
be either scheduled by an eNB or selected in an autonomous manner with the
help of sensing. The sensing is performed based on a combination of priority
information, energy sensing, and sidelink control channel decoding. Congestion
control is also supported such that resource utilization becomes restricted
upon congestion based on a channel busy ratio (CBR). Prioritization between
the access link transmissions and V2X transmissions may be necessary.
Additional enhancements for V2X were also done in LTE in Release 15 [142],
focusing on aggregating multiple carriers and enhancing autonomous resource
allocation.

The work on 5G NR V2X started in Release 16 [143]. 5G NR V2X is designed to be
backward compatible at upper layers so that it can co-exist with LTE V2X (as
in Releases 14 and 15). In particular, Release-16 V2X is designed to support
Release-14 and Release-15 V2X for safety use cases. Additionally, Release-16
V2X enables new services such as real-time updates, and coordinated driving,
especially those requiring high reliability and QoS support. Different from
Release-14 and Release-15 V2X where only broadcast messages are supported,
Release-16 V2X additionally supports groupcast and unicast. Spectral
efficiency is significantly improved, e.g., via higher modulation order (up to
256-quadrature amplitude modulation (256QAM)) and MIMO transmissions (up to
two layers). Similar to the access link, flexible numerologies are also
supported, e.g., with subcarrier spacing of 15, 30, 60, and 120 kHz. To cope
with extremely high mobility, a range of DMRS patterns are supported with
different time-domain densities.

Enhancements in Release-17 NR V2X [144] are mainly focused on two aspects:
power savings and resource allocation. Power savings are achieved via the
support of partial sensing and sidelink DRX operations. Resource allocation
can be improved significantly when two or more UEs coordinate with each other
and share the resource availability/non-availability information, where the
information may be assessed based on various reasons (e.g., sensing and
capability). These resource allocation enhancements may bring improved
reliability, reduced latency and reduced UE power consumption.

NR sidelink will be further enhanced in Release 18 in a more expanded manner
[145, 80, 146], including the following areas:

  * •

Sidelink carrier aggregation (CA) operation;

  * •

Sidelink in unlicensed spectrum;

  * •

Enhanced sidelink operation for FR2 licensed spectrum;

  * •

Mechanism(s) for co-channel coexistence for LTE sidelink and NR sidelink;

  * •

Sidelink relay enhancements;

  * •

Sidelink positioning and ranging.

With the above items, it is expected that not only the support of C-V2X can be
more comprehensive and effective, but also the support of other services
(e.g., network controlled interactive services via sidelink) can be enabled or
enhanced.

###  VII-B C-V2X for Safety Applications

Given the impelling need to reduce car accidents and traffic fatalities
involving vulnerable road users (VRUs), such as pedestrians, bicyclists and
e-kick scooters, safety applications enabled by mobile communications have
become crucial to ITS. Such applications are particularly relevant in the
presence of obstacles, such as buildings, that prevent a driver or the sensors
aboard an autonomous/automated vehicle from realizing the danger in a timely
manner. Most of these applications leverage such messages as the ETSI
cooperative awareness messages (CAMs) that can be periodically broadcasted by
the vehicle’s radio interface and include the vehicle’s position, speed,
acceleration, and heading [147]. Additionally, as discussed in [148], safety
applications can often benefit from messages generated by road infrastructure
or smart city entities (e.g., cameras, and traffic controllers or dynamic map
generators located at the edge of the network infrastructure), as they can
effectively complement the local view that each single vehicle conveys through
the transmission of, e.g., CAMs. Several studies have therefore appeared in
the literature proposing solutions for safety applications enabled by C-V2X,
where the X stands indeed for everything, including other vehicles or VRUs,
the network infrastructure, or an edge server. The recent focus on C-V2X as
communication technology for connected cars has also been motivated by two
important facts. Firstly, as reported in the previous section, the 3GPP C-V2X
standardization efforts aim at meeting increasingly challenging requirements
for advanced services. Secondly, there is a clear regulation shift towards
C-V2X, confirmed by, e.g., the Federal Communications Commission (FCC)
regulations that have changed the use of the 5.9 GHz frequency band reserved
for ITS operations from dedicated short range communications (DSRC) to C-V2X.

A useful taxonomy of the types of V2X communication and the applications that
they can support can be found in [149] and is highlighted in Table III.
Specifically, the table lists the main safety applications enabled by C-V2X
and the type of communication they require, ranging from periodic V2V/V2I/V2P
messages, to V2V/V2I event-driven messages, V2V bi-directional and multi-hop
communication, to V2P/V2N bi-directional communication.

TABLE III: Road safety applications supported by different message types Message Type |  Supported Applications
---|---
V2V periodic |  Cooperative collision warning, intersection movement assistance, slow vehicle warning, cooperative glare reduction, collective perception
V2V event-driven |  Stationary vehicle warning, emergency electronic brake lights, queue/traffic jam ahead warning, road condition warning
V2I bi-directional |  See-through
V2V multi-hop |  Coverage extension of ITS messages
V2P periodic |  Pedestrian collision warning to vehicles, vehicle collision warning to pedestrians
V2I periodic |  In-vehicle signage, curve speed warning
V2I event-driven |  Infrastructure based collision warning, warning of vulnerable road user presence, infrastructure based traffic jam ahead warning, infrastructure based road condition warning, roadwork warning
V2N bi-directional |  Dynamic map download and update

Relevant examples of solutions appeared in the literature, targeting the
support of safety applications through the aforementioned types of messages,
include [150] where V2V periodic messages allow for collective perception
sharing based on Release-14 LTE PC5 mode 4, and [151] which envisions mobile
base stations deployed in UAV to improve the connectivity offered to vehicles
by terrestrial base stations. The use of V2V event-driven messages, combined
with CAM transmissions, has been instead leveraged in [152] to implement a
cooperative lane merging application using the LTE PC5 mode 4 technology.
Interestingly, [153] and [154] have highlighted the limitations of the
configuration of PC5 mode 4 interfaces in Release-14 LTE, and, especially, of
the autonomous sidelink resource allocation algorithm to transmit V2V event-
driven messages – an issue that has then been overcome in Release-16 NR PC5
mode 2 [155]. For example, the sensing window of 1 s used in Release-14 LTE
PC5 mode 4 works well for V2V periodic messages, but it is too long for
asynchronous traffic, in which case a 100 ms-window turns out to perform much
better. Furthermore, it is worth mentioning that the combination of V2V
periodic and event-driven messages have been successfully leveraged to also
support platooning applications [156]. As far as V2V bi-directional messages
are concerned, they are useful in the see-through application, where a vehicle
requests and receives light detection and ranging (LiDAR) or camera data from
another vehicle, so it can “see” beyond the visibility range of its radio and
sensors. To support the exchange of such bi-directional information, [157] has
investigated the use of mmWave communications, while the results of field
tests with pre-5G NR Uu interfaces have been reported in [158]. Finally, V2V
multihop messages have been used mainly to extend the propagation of safety
messages in vehicular networks, as in [159] where vehicle clustering and
Release-14 LTE PC5 mode 3 were used.

Many safety applications for vehicles and VRUs are also supported through V2I
messages, which are periodic and event-driven, and sent from vehicles to the
infrastructure and then re-broadcasted by the latter through infrastructure-
to-vehicle (I2V) or infrastructure-to-pedestrian (I2P) messages so that they
can be disseminated over a larger area and leveraged at each mobile entity as
input to, e.g., a collision avoidance algorithm [160, 161, 162, 163]. I2V
messages can also be effectively used for in-vehicle replication of road
signaling (e.g., traffic light information) and to support autonomous driving
by providing vehicles with information generated by road infrastructure
entities or traffic controllers [164, 165], as well as for dynamic map
download and update [166].

###  VII-C Future Evolution

5G NR-based sidelink enables UE-to-network and UE-to-UE relay to significantly
improve network performance for C-V2X and public safety. Looking ahead to 6G,
it is possible for sidelink to expand existing C-V2X capabilities, to support
new use cases such as aerial networking with flexible network architectures.
Furthermore, on top of the single-hop relay functionality that has already
been supported by the existing 3GPP sidelink, multi-hop routing/networking
will allow multiple UEs and UAVs to connect and create a mesh network under
various coverage scenarios for different applications and use cases.

mmWave D2D relay has been introduced in [167] to improve the coverage and
spectral efficiency of a wireless network. With the help of D2D relay, it is
possible to form a mesh network with dynamic network topologies and
architectures. Meanwhile, it is also possible to create a three-dimensional
(3D) mesh network by incorporating UAVs and NTN nodes such as high altitude
platform stations (HAPS) and satellites. For example, a swarm UAV network has
been introduced in [168] for substantial throughput enhancement. Recently,
there is also a tremendous interest in planning novel LEO satellite systems,
called mega-constellations, to form a multi-layer mesh network [167]. Routing
and resource allocation are critical for mesh networks and these problems will
become even more challenging in a 3D network with multiple layers.
Traditionally, cooperative communication and routing strategies [169, 170,
171, 172] have been introduced for D2D mesh networks to improve the underlying
network performance. Random network coding-based cooperative routing
strategies have also been introduced in swarm UAV networks to reduce
transmission delay with increased communication reliability [173]. For more
complicated multi-layer mesh networks, it is envisioned that AI/ML could be
adopted to adjust routing paths [174], to conduct distributed and dynamic
resource allocation [175], and to optimize handover decisions within layers
and among layers of the 3D NTN architecture [176].

##  VIII AI/ML EVOLUTION

In this section, we first provide an overview of the 3GPP management system
for network slicing and data analytics. Then we introduce the 3GPP work on
AI/ML for NG-RAN and NR air interfaces. After that, we discuss AI-native air
interface towards 6G.

###  VIII-A Management System and Data Analytics

One of the key features of 5G is the ability to support network slicing, i.e.,
to create multiple logical networks on a shared infrastructure spanning over
the core network till the radio access network. Each network slice is composed
of one or more NFs, which can be physical or virtual (PNF and VNF,
respectively). Furthermore, each network slice is characterized by a set of
key performance indicators (KPI) and, possibly, isolation requirements. In
order to deploy, orchestrate, and manage a network slice, hence the NFs
therein, 3GPP has defined a 5G management system, as illustrated in Fig. 9.

Figure 9: Network slice subnet management with interface to NFV-MANO [177].

A network slice subnet (NSS) provides the management aspects of the NFs
managed through the 5G management system along with the corresponding required
computing, memory, and networking resources [177]. The set of NFs, either
physical or virtual, composing a network service, represents the actual
implementation of an NSS, i.e., a network slice subnet instance (NSSI). The
NSS management system in Fig. 9 includes the main components, namely, the NSS
management function (NSSMF) and the NF management function (NFMF), as well as
the interface with another key element of the 3GPP network functions
virtualization (NFV) architecture, i.e., the NFV-management and orchestration
(MANO).

Importantly, the 3GPP Release-17 management and orchestration architecture
framework [177] also introduces a service based management architecture (SBMA)
for the 5G management system, as depicted in Fig. 10. This comprises
management functions (MFs) that provide multiple management services (MnS) to
manage single functions (NFMF), network slice subnets (NSSMF), communication
services (CSMF), and the exposure of such services towards external entities
(EGMF). It is worth noticing that the SBMA also comprises the management data
analytics function (MDAF), which is in charge of delivering analytics services
for automated network management and orchestration. Such data-driven decisions
drive the logic of the NSMF and NSSMF, which manage the lifecycle of the
network slices and the orchestration of their resources.

Figure 10: Example of functional management architecture [177].

It is also worth to remark that the MDAF receives as input monitoring data
collected from the network (gNBs in the RAN or specific core network
functions) and its management system on the existing NSS or VNF performance,
as well as analytics data offered by the network data analytics function
(NWDAF) [178]. In other words, MDAF and NWDAF operate at the management and at
the core network level, respectively, with, e.g., the former being concerned
with information on the load level of a slice instance across RAN and core
network domains, while the latter with data and control in the core network
domain. The MDAF then provides analytics results to drive the decisions
related to the NSS life cycle management and the NS orchestration, including
RAN and core network domain-related decisions. In particular, the NWDAF not
only produces analytics related to the core network domain and network
functions and feeds them to the MDAF, but it also leverages the MDA reports to
control the core network. Interestingly, a 5G network can feature more than
one instance of NWDAF, each associated with a different service area and
possibly specialized in providing a certain type of analytics, and with one of
the deployed instances acting as an aggregator [179].

###  VIII-B AI/ML for 5G Radio Access Networks

Data driven techniques such as AI/ML-based solutions can be a powerful tool to
address the challenges of 5G RANs. 5G RANs need to address complex system
design and large network optimization problems to meet a wide range of
performance requirements, including coverage, data rate, latency, reliability,
and user experience. Though a vast number of AI/ML applications in RAN can be
up to proprietary implementations and solutions, investigating the needed
standards support is essential to accelerate the use of AI/ML techniques in 5G
RAN and beyond [180, 181].

In Release 17, 3GPP conducted a study to investigate the functional framework
for RAN intelligence enabled by further enhancement of data collection [182].
The study identified a set of high-level principles for AI-enabled RAN
intelligence, such as focusing on AI/ML functionality and corresponding types
of inputs and outputs while leaving the detailed AI/ML algorithms and models
to implementation. Leaving AI/ML algorithms and models to implementation can
incentivize vendor competitiveness. The study also introduced a functional
framework for RAN intelligence and investigated a set of use cases, including:

  * •

Network energy saving: AI/ML algorithms can predict traffic load and energy
consumption by leveraging the data collected in RAN. The prediction can be
used to help decide cell activation and deactivation strategies to improve
network energy efficiency [183].

  * •

Load balancing: AI/ML algorithms can predict traffic load and automate the
optimization of mobility management parameters to improve user experience and
system capacity [184].

  * •

Mobility optimization: AI/ML algorithms can enhance mobility performance by
reducing the probability of unintended events (e.g., handover failure, radio
link failure). AI/ML algorithms can also predict UE location and mobility
trajectory, which can serve as valuable inputs for RRM and traffic steering.

After completing the Release-17 study on AI-enabled RAN, 3GPP continues to
conduct the corresponding normative work in Release 18 [185]. The Release-18
work item aims to specify data collection enhancements and signaling support
for AI/ML-based network energy saving, load balancing, and mobility
optimization. The enhancements will be introduced within the existing 5G RAN
interfaces and architecture. Once the normative part is completed, additional
uses cases are expected to be taken under study as part of a new study item
for AI-enabled RAN.

To reap the full benefits of AI/ML for 5G, exploring the potential of
augmenting the NR air interface with AI/ML-based features is indispensable. To
this end, 3GPP Release 18 includes a study item that investigates AI/ML for NR
air interface to improve performance [90]. The study aims to set up a general
framework for AI/ML related enhancements for air interface, such as
characterizing the defining stages of AI/ML related algorithms and the life
cycle management of AI/ML models, exploring different levels of collaboration
between UE and gNB, and investigating needed datasets for AI/ML related
enhancements for NR air interface. The study takes a use case-centric
approach, focusing on the follow three use cases:

  * •

CSI feedback enhancements: AI/ML algorithms can be used for CSI compression in
frequency domain and CSI prediction in time domain to reduce overhead and
improve accuracy.

  * •

Beam management: AI/ML algorithms can be used for beam prediction in spatial
and time domains to reduce overhead and latency and improve beam selection
accuracy.

  * •

Positioning accuracy enhancements: AI/ML algorithms can be used for direct
AI/ML positioning (e.g., fingerprinting) and AI/ML-assisted positioning (e.g.,
the output of the AI/ML model inference is a new measurement or an enhancement
of an existing measurement) to improve positioning accuracy.

For each use case, the study aims to establish evaluation methodologies and
determine KPI to thoroughly evaluate the performance benefits of AI/ML
algorithms for the NR air interface. Last but not least, the study assesses
the potential specification impact of AI/ML-related enhancements for NR air
interface, including physical layer aspects, protocol aspects, and
interoperability and testability aspects. Normative work is foreseen for
Release 19.

###  VIII-C AI-native Air Interface

Moving over 3GPP Release-18 5G-Advanced towards 6G and beyond, it is important
to consider AI-native air interface where we will rely on AI/ML models and
tools to design individual network components or a purely end-to-end
communication system. This paradigm shift in the approach of network design
represents a potential disruption to the established cellular systems design
procedure, extending to the deployment and operation stages as well.

####  VIII-C1 AI-native end-to-end design for the physical layer

The receiver block in the physical layer is the focus of most research efforts
in infusing AI/ML-based solutions to overcome issues such as model mismatch
and complexity in traditional model-based processing [180]. For example,
AI/ML-based techniques have been used to efficiently combat the non-linear
distortion created by power amplifier [186] and low-resolution ADC [187]. They
have also been applied to transmitter blocks with the intent of optimizing
performance compared to and in addition to existing closed-loop feedback
methods. For example, [188] uses deep learning for combined channel estimation
and hybrid precoder design. The AI approach, however, requires that ML methods
are used fundamentally to design the entire chain which includes the
transmitter, channel and the receiver. There exists work in end-to-end
physical layer design which aims to jointly train the transmitter and the
receiver, while catering to simpler additive white Gaussian noise (AWGN) [189]
or Rayleigh block fading channel [190] scenarios. Built on this, [191] has
developed a jointly trained neural network based transmitter and receiver that
achieves the state-of-art bit error rate without needing pilot transmissions
in practical wireless channel models. Although promising, the AI/ML-based
receiver still needs to be trained offline for a significantly large number of
frames, which may be an impediment to practical adoption since an online-based
operation is critical for cellular networks especially when the adaptation is
on-the-fly.

####  VIII-C2 AI-native design for the MAC layer

Given the complex nature of tasks that the MAC layer is responsible for
including, but not limited to, user selection, random access, resource
allocation, etc., the solutions to these are mostly heuristic in nature. Since
it is extremely challenging to identify optimal solutions for these problems
in real-time, there is a large potential to leverage AI/ML-based solutions in
addressing MAC layer problems. In this context, reinforcement learning usually
finds itself best placed as the ideal tool which can handle the time-varying
network conditions that may be attributable to the users or the channel. There
are examples of ML-based solutions applied to a wide gamut of MAC layer
operations such as reinforcement learning-based approaches for random access
[192] and for spectrum access/sharing [193], federated learning for minimizing
breaks in presence in wireless VR [194], and predictive resource allocation in
IoT networks [195]. However, it is fair to say that a truly “AI-native” MAC
design that learns and evolves over time still needs significant work. In some
ways, this involves learning an entire protocol from scratch and would need to
utilize tools from deep multi-agent reinforcement learning [196]. Furthermore,
sample efficiency and convergence rate of the corresponding reinforcement
learning tools need to be significantly improved for them to be relevant for
practical networks for 6G and beyond to realize AI-native design.

##  IX DUPLEX EVOLUTION

In this section, we will first provide an overview of duplex evolution in
3GPP, starting from the standardization of traditional frequency division
duplex (FDD) and semi-static TDD operations, followed by dynamic operations
for better traffic adaption, and the most recent study on simultaneous
transmission and reception at a gNB. We will then explore the related research
outside 3GPP.

###  IX-A Duplex Evolution in 3GPP Standards

####  IX-A1 FDD vs. TDD

FDD and TDD have been the two duplex technologies adopted in 3G, 4G, and 5G
standards of 3GPP. In FDD, a paired spectrum is allocated for DL, where a base
station transmits and a device receives signals, and UL, where a device
transmits and a base station receives signals. TDD does not require a paired
spectrum and partitions the spectrum into DL and UL in the time domain.

It would be natural to consider FDD for the case of symmetric traffic between
DL and UL, e.g., when voice calls dominate the wireless traffic. Furthermore,
the simultaneous existence of DL and UL using a paired spectrum enables simple
and efficient system operations. An example is that a device can send to its
base station an acknowledgement of success or failure of DL data reception
while keeping a fixed time interval between DL reception and UL transmission
as specified in 3G high speed downlink packet access (HSDPA), 4G LTE, and 5G
NR standards [197, 198, 199].

TDD has the benefit of offering flexible deployments with an unpaired spectrum
and hence becomes more attractive when it is difficult to find a pair of
frequency bands. This is a major reason for choosing TDD to deploy 5G systems
using a new spectrum such as 3.5 GHz or 28 GHz bands where it is difficult to
find a paired spectrum. Furthermore, the channel reciprocity required for
operating massive MIMO without excessive feedback overhead is an important
advocate for adopting TDD.

Provisioning of different configurations of DL and UL resources in time domain
depending on the traffic demand of each link has been understood as an
important benefit of TDD. In practical TDD cellular systems with macro cell
deployments, on the contrary, it has been typical that a pre-defined DL-UL
resource partitioning in time domain and the same transmission direction (UL
or DL) are maintained in all cells of the network of a mobile network operator
(MNO) to prevent the cross-link interference between DL and UL of neighboring
cells. A base station’s DL transmission could cause serious interference to UL
reception of a neighboring base station. The UL transmission from a UE of a
cell could become a strong interference to the DL reception of another UE of a
neighboring cell especially when they are located closely in a cell boundary
area. Furthermore, MNOs using adjacent bands typically align their DL-UL
configurations to prevent interference between their networks. With the
increasing amount of mobile traffic generated in hotspots and indoor
environments, there is increasing interest in heterogeneous networks
consisting of macro and small cells. In such scenario, it could become more
useful to have dynamic adaptation of DL and UL allocation in TDD operation of
a small cell depending on its own traffic situation independently.

####  IX-A2 LTE enhanced interference mitigation and traffic adaptation
(eIMTA)

3GPP made standards enabling the adaptation of configuration of DL and UL
subframes per 10 ms frame duration in LTE Release 12, which was known as eIMTA
[200]. Additional measures for handling of the cross link interference (CLI)
between DL and UL were specified in LTE standards [198]. The eIMTA operation
could cause severe variations in SINR between consecutive transmission time
intervals even though the radio channel does not change. To have proper
awareness of SINR fluctuation due to eIMTA, separate CSI measurements for the
DL subframe, and the flexible subframe that can be either DL or UL were
introduced. In order to avoid the severe performance loss due to the CLI, UL
power control was enhanced to allow having different transmit power levels
between the fixed UL subframe and the flexible subframe.

####  IX-A3 NR dynamic TDD

As a further evolution of TDD operation in 5G, 3GPP specified dynamic TDD
feature in Release 15 NR standard, where L1 signaling of slot format
indication designates each symbol of a slot to be DL (‘D’) symbol, UL (‘U’)
symbol, or flexible (‘F’) symbol according to Table 11.1.1-1 in [199]. ‘F’
symbol could be either DL or UL depending on the gNB’s scheduling decision and
the transmission direction is indicated to the UE via a L1 control signaling.
A slot consists of 14 symbols and 56 slot formats are defined to support
various combinations of ‘D’, ‘U’, and ‘F’ symbols in a slot. It can be seen
that the dynamic TDD feature of NR supports more frequent and flexible
switching between DL and UL direction in time than the LTE eIMTA.

For the CLI handling, two UE measurements were specified in Release 16 [201]:
(1) SRS reference signal received power (SRS-RSRP) which can be utilized by
gNB to estimate the interference from a UE’s uplink to another UE’s downlink;
and (2) CLI received signal strength indicator (CLI-RSSI) which can be
utilized by gNB to estimate the total amount of CLI experienced by a UE.

####  IX-A4 Duplex evolution in 5G-Advanced

To further enhance the NR TDD operation in 5G-Advanced, 3GPP is performing a
study on evolution of NR duplex operation in Release 18 [202], which consists
of two major subjects. The first subject is enhancement on the dynamic TDD to
address, e.g., the gNB-to-gNB CLI, which may be caused due to either adjacent-
channel CLI or co-channel CLI, or both, depending on the deployment scenario.
The second subject is the feasibility of allowing the simultaneous existence
of DL and UL, a.k.a., full duplex.

Full duplex can create the CLI as observed with dynamic TDD as well as the
self-interference from Tx to Rx. The significance of the self-interference
depends on the frequency allocation for Tx and Rx as illustrated in Fig. 11,
where the left case shows transmission and reception using the non-overlapping
frequency while the right one shows transmission and reception using the
overlapping frequency. Considering the expected complexity for mitigating the
self-interference from Tx to Rx and the CLI, the study assumes full duplex
with non-overlapping allocation of DL and UL subbands at the gNB side while
the UE performs the conventional TDD operation. Such approach could make the
continuous UL transmission possible in the TDD system that is widely used in
commercial NR deployments, which would be beneficial to improve coverage,
latency, and capacity of the NR TDD system.

The non-ideal spectrum shaping and nonlinearity of power amplifier at Tx
creates the self-interference and CLI to Rx even though DL and UL use
different subbands. Separation of transmission and reception antennas possibly
with a special structure for signal isolation between them and use of proper
beamforming at Tx and Rx can achieve significant reduction of the self-
interference. The remaining self-interference could be further suppressed by
use of RF filters and digital signal processing at Rx. The Rx RF filter with
sharp spectrum shape can also help reduce the CLI from adjacent carriers. On
the other hand, the CLI caused by the transmission in the same carrier,
especially DL transmission with high transmission power from a neighbour base
station site to UL reception, would be much harder to mitigate. This property
of CLI may make it easier to apply full duplex in the small cell deployment,
where small cells (or small cell groups) are isolated so that the same carrier
CLI can be easily avoided. For the wide-area macro cell deployment, proper
coordination of transmission direction or UL-DL frequency separation between
neighbouring base station sites would be necessary.

###  IX-B Full Duplex Outside 3GPP

Full duplex, also known as in-band full duplex (IBFD) where transmission and
reception occur on the same frequency band, was first utilized as early as in
1940s in the context of full duplex radars [203]. Different from the FDD or
TDD duplexing method when transmission and reception are sufficiently
separated, full duplex imposes daunting challenges to transceiver
implementations due to self-interference (i.e., a receiver is interfered by
its own transmitter due to leakage) and potential CLI, as discussed earlier.

Figure 11: Self-interference from transmission to reception.

There are numerous comprehensive reviews on IBFD in the literature [204, 205,
206, 207, 208, 209]. Reference [204] focuses on full-duplex radars and IBFD
wireless communications, presenting the corresponding opportunities and
techniques for self-interference reduction. Reference [205] provides a
comprehensive tutorial on IBFD from the perspective of physical and MAC
layers. It summarizes the benefits, different topologies (i.e., bi-directional
full duplex, full duplex relay, and full duplex cellular in a multiple access
setting), challenges in self-interference cancellation, and IBFD for other
purposes. Reference [206] compares more than 50 demonstrated IBFD
communication systems with more than 80 different measurement scenarios, in
terms of the corresponding isolation performance with respect to the center
frequency, instantaneous bandwidth, and transmit power. A set of self-
interference cancellation techniques are also summarized therein. Reference
[207] presents a survey on IBFD in relaying scenarios, which covers the
enabling technologies, key design issues, basics of IBFD, challenges and
broader perspectives, and performance analysis. The tutorial in [208]
concentrates on wireless communications, listing the potential benefits
offered by full duplex techniques, surveying the critical issues related to
full duplex transmissions from a physical-layer perspective relying on self-
interference suppression/cancellation, while giving cognizance to the MAC-
layer protocols, investigating the main hardware imperfections, and discussing
the advantages, drawbacks, as well as design challenges of practical full
duplex systems, and identifying their new directions and applications.
Reference [209] contains a comprehensive survey on self-interference
cancellation techniques in the antenna/propagation domain. It also discusses
the opportunities and challenges of employing IBFD antennas in future wireless
communication networks.

Besides radar and wireless communications particularly for 5G NR and 6G, full
duplex also finds applications in many other areas, e.g.:

  * •

Cognitive radio networks, where full duplex operations enable simultaneous
information sharing and sensing for improved spectrum sharing [210, 211];

  * •

Physical layer secrecy, where full duplex helps activate more simultaneous
transmissions or introduction of jamming noise signals, thus creating
additional interference to eavesdroppers leading to the increased so-called
secrecy capacity (i.e., the highest possible data rate at which a secret
transmission can be reliably conveyed without being eavesdropped) [212];

  * •

Wireless power transfer, where simultaneous wireless energy transfer (e.g.,
via DL) and wireless information transfer (e.g., via UL) can be supported,
which is important for energy-constrained wireless communication systems
(e.g., wireless sensor networks) [213].

The challenges and potential solutions for IBFD were well studied in the
literature [204, 205, 206, 207, 208, 209]. Effective and efficient self-
interference and/or inter-node interference suppression/cancellation are
critical and challenging for practical IBFD operations. There are many factors
that need to be taken into account, e.g.:

  * •

Time-varying channels;

  * •

Single or multiple streams (i.e., MIMO);

  * •

Potential presence of multiple transmitters and receivers (e.g., in cellular
networks, where the issue is more pronounced in case of heterogenous
networks);

  * •

Hardware imperfections (e.g., phase noise, non-flat frequency response of
circuits, power amplifier nonlinearity, transmit I/Q imbalance);

  * •

Reasonable power consumption (especially for battery-powered devices) and
complexity (e.g., to ensure small-size circuits).

The enabling techniques for interference suppression/cancellation can be
generally categorized into the following domains [206]:

  * •

Propagation domain, which may be comprised of passive, active and antenna
interfaces;

  * •

Analog domain, where the approaches may be in time-domain, frequency-domain,
or digitally assisted;

  * •

Digital domain, where various modelling may be assumed (linear/nonlinear,
reference-based by utilizing an auxiliary receive channel, receiver
beamforming, etc.).

The study of the evolution of NR duplex operation in Release 18 in 3GPP marks
an important step for practical IBFD operations. It is expected that the study
would result in successful identification of the issues, benefits, and
realistic solutions for IBFD, making it possible for commercial deployments in
the future.

##  X ENERGY EFFICIENCY EVOLUTION TOWARDS GREEN NETWORKS

In this section, we focus on power consumption in wireless systems, which is
one critical design aspect. Realizing green networks needs to consider both
sides: UEs and base stations (network side). We first discuss UE side power
consumption that may have direct impact on end-user experiences and thus has
always been actively pursued and standardized in 3GPP. Then we consider the
power consumption at the network side, which is crucial for network
sustainability and vital for going forward.

###  X-A UE Side Power Consumption

Power consumption for battery-powered end devices is an important performance
metric. 3GPP standardization pays close attention to UE power consumption and
has standardized various power saving techniques to improve UE power
consumption, especially considering different UE types and operation
conditions, even when there are conflicting goals.

5G NR is designed to support various UE types. Smart phone users are of course
one primary target, which is often linked with the continued advancements on
eMBB services [214]. These devices are generally highly capable with advanced
processing power. MTC often aims to support low cost and low complexity
devices, covering a wide range of applications such as metering, road
security, and consumer electronic devices [1]. Different from smart phones,
MTC devices generally require very long battery life, e.g., as long as 10
years [1], since these devices may not be easily replaced due to the
associated cost and operation environments. In order to more efficiently
support services for connected industries, smart city innovations and
wearables (e.g., smart watches), a new type of devices called RedCap devices
[215], with requirements lower than eMBB but higher than the traditional MTC,
was recently standardized.

Power savings for these devices are supported for different operation states,
in particular, idle and connected states. An idle state UE is when the UE
camps on a cell but without an established radio resource control (RRC)
connection. Power saving techniques may include idle discontinuous reception
(I-DRX) [1], advanced paging techniques (e.g., group-based wake-up signal
(WUS) for power savings [1]), relaxed measurements and reduced mobility
requirements [215]. An RRC connected UE may benefit from connected state DRX
(C-DRX), physcial downlink control channel (PDCCH) based WUS, and cross-slot
scheduling (where a UE may perform the so-called “micro-sleep” if not
scheduled [216]), restriction of low-rank MIMO operation when appropriate,
reduced or skipped control channel monitoring, and fast activation and
deactivation of a carrier or a configured scheduling. Monitoring PDCCH can be
skipped or dynamically switched among a few configured search space set
groups. A secondary cell may also be placed into a so-called dormancy mode as
part of the activated state, where a UE is not required to monitor PDCCH for
the secondary cell, but can be quickly indicated to transition from the
dormancy mode to non-dormant mode and vice versa. A UE may also provide
assistance information for power savings, such as a set of preferred power
saving parameters including DRX configurations, a maximum aggregated
bandwidth, and a number of carriers. For power savings, a UE can also have
relaxed RRM, radio link monitoring, or beam failure detection requirements
especially if its mobility level is low.

The goal for UE power savings may be in conflict with other design goals,
e.g., capacity or coverage. As an example, an MTC user may need coverage
enhancements as high as 20 dB [1], which may require transmissions of the same
information over an extended duration. Such repeated transmission due to the
increased coverage need is not friendly to UE power consumption. Similarly, a
UE with restricted MIMO operation is not capacity-friendly.

More sustainable UE operations can benefit from energy harvesting, especially
for use cases such as health and fitness tracking, environment monitoring, and
transportation [217, 218, 219, 220, 221, 222, 223, 224]. Energy harvesting may
be particularly beneficial and associated with passive IoT devices such as RF
identification (RFID) [222]. The mechanisms may be solar-based, wind-based,
vibrational, electromagnetic, thermoelectric, or based on ambient RF energy
[217, 224]. The challenges for RF energy harvesting may include overall RF
power conversion efficiency, form factor, operational bandwidth, and
compactness [219]. Reference [220] provides a summary of the RF energy
harvesting in the past six years.

###  X-B Network Side Power Consumption

With the need to reduce the carbon dioxide (CO2) emissions and on the other
hand to reduce the network operating cost, while facing continuous increase in
data volume to be carried by the networks, improvements in the network energy
efficiency is a must. Unlike the handset total energy consumption, the major
part of the network energy consumption occurs during the lifetime of the
network infrastructure instead of the actual manufacturing phase. The
development with 5G has already reduced especially the static power
consumption compared to 4G or 3G, where networks had to be transmitting radio
signals more frequently or even continuously even if there were no active data
transmission taking place. With 5G the network side already took major step
towards energy efficiency from continuous transmission with 3G or transmission
every millisecond with 4G. This was also a requirement for the International
Mobile Telecommunications-2020 (IMT-2020) technology [225].

The key design choice with 5G was to move away from the common reference
signals sent with such a frequency that one could not really drive down the
power hungry RF parts even in case of lack of traffic. 3G networks were even
worse with the common pilot channel which was sent continuously. The next
steps in power efficiency on the network side are related to avoiding all
transmissions when there is no actual user data to be transmitted, and using
power efficient waveforms when having to transmit something. This together
with other energy saving features allows 5G to be 10X more energy efficient
than 4G in terms of energy consumed per traffic unit, but there still remains
more that could be done as part of the 5G-Advanced.

As part of the 3GPP 5G-Advanced studies [226], the network energy consumption
model is to be established, which then should enable vendor independent
consideration of network power consumption impact when adding support for a
new channel or signal to be transmitted or some new procedures between network
elements. With the help of the energy consumption model, one is able to assess
for example what would be the network side price to pay for a special signal
to drive a low power wake-up radio at the UE side. This should be done to come
up with sustainable designs for new features, which for the sake of saving on
one side, would not cause major impacts on the energy efficiency in other
dimensions.

The best energy saving is achieved when one can shut down an entire base
station, especially first shutting down and replacing the earlier generations
that consume lots of power regardless of the traffic level. Ideally, with only
5G remaining, one should optimize the number of frequencies to be active for
the coverage and traffic support needed at a given time in the area of
interest, as well as the number of antenna ports active at the network side.
Especially under low traffic load, the network could afford to use only a
subset of antenna ports in the case of an antenna array, reducing the number
of ports from, e.g., 64 to 16. AI/ML based control is expected to be the key
enabler for this, allowing for dynamically coordinated energy saving
operations across different network layers, so as to minimize the network
energy consumption in 5G [226]. As indicated in [226], 70% of operator energy
consumption occurs at the base station site. Under low traffic load, the
possibilities are limited as the current solutions achieve most of the gains,
and reducing the base station activity further would mostly make it even more
challenging to find and measure signals timely at the UE side, with
diminishing gains as highlighted in [227].

The detailed solutions now part of the Release 18 scope for the network side
aim to add operation without synchronization signal block (SSB), i.e., SSB-
less, for secondary cell (SCell) operations for the inter-band CA case, as
well as enhancing the cell discontinuous transmission (DTX)/DRX mechanism. For
the spatial domain, adaptation of the number of spatial elements is to be
enhanced. In the power domain, the power offset between physical downlink
shared channel (PDSCH) and CSI-RS is to be adapted for lower energy
consumption based on improved feedback to allow for more optimized PDSCH power
setting at the gNB side. For the protocol side, conditional handover is to be
enhanced to cover the case when either the source or the target cell is in the
network energy saving mode.

Furthermore, there are various research directions on the improvements of
network energy consumption. One of the major factors worth to investigate is
the impact of network scheduling solutions. Besides the classical network
performance, network power consumption is also impacted by the network
scheduling approach, especially in 5G. In the case of low traffic load,
ideally the scheduler would also send data only very sparsely, however in 5G
it is essential to pay attention to the resulting latency impact as well. The
scheduling impact on network power consumption has been studied for example in
[228] and in [229] for a given QoS level. Another dimension to consider is the
use of heterogeneous networks with small and pico cells for improved energy
efficiency to achieve the needed total capacity [230]. As small cells operate
with lower power levels, they can offer capacity boost with good energy
efficiency. In general, the use of self organizing networks (SONs) or lately
AI/ML to obtain improvements in network energy efficiency has been discussed
for example in [231], and also in actual deployments, as there are many
network operation elements one can deploy without impact on the standards
[226]. It should be noted that when considering energy efficiency
improvements, the energy cost for the enablers themselves such as AI/ML
training and inference needs to be taken into account as well.

##  XI ADDITIONAL 6G ASPECTS

In the previous sections, we have discussed several 6G related aspects when
introducing the key technology evolution directions in 5G-Advanced. The 6G
discussions we have provided are by no means exhaustive. In this section, we
point out some additional 6G aspects, including spectrum evolution, JCS for
6G, and provision of hyper-distributed, innovative 6G services.

###  XI-A Spectrum Evolution

To alleviate the scarcity of the spectrum as a precious resource, the concept
of full spectrum is being propounded for 6G, which covers sub-7 GHz,
centimetric range from 7-20 GHz, mmWave, THz, and optical frequency bands.

The sub-7 GHz bands have been the dominant frequency spectrum in 4G and 5G,
and will continue playing an important role in 6G, since they are naturally
well suited to provide ubiquitous coverage and reliable wireless connection
[232]. Nevertheless, the sub-7 GHz spectrum has been crowded and thus new
spectrum is in need for future radio communications.

The pioneer spectrum blocks for 6G are expected to be in the centimetric range
from 7-20 GHz for both higher data rates (as opposed to the sub-7 GHz band)
and acceptable coverage, and the mmWave bands for peak data rates exceeding
100 Gbps. For example, the Federal Communications Commission (FCC) expressed
the need to support 6G service in the mid-band spectrum such as the 7-16 GHz
range and started to explore repurposing spectrum in the 12.7-13.25 GHz band
for next-generation wireless technologies in the U.S. [233]. Meanwhile,
extensive research and trials have been carried out for the mmWave bands which
have verified its great potential in cellular communications when combined
with beamforming techniques. Thanks to its large bandwidths and small
wavelengths (compared with lower frequency bands), mmWave is able to provide
high temporal and spatial resolution, facilitating high-precision object
localization and tracking.

In addition, with the explosive growth of traffic globally, the THz spectrum
is considered a promising new frequency range for 6G to provide even larger
bandwidth and more abundant spectrum resources. In 6G wireless networks,
typical application scenarios of THz communication may include indoor
communication, hotspot downloading, wireless data center, fixed wireless
access and wireless cellular fronthaul/backhaul, and security communication
scenarios. However, based on the current state of technology, some critical
challenges still need to be solved for THz communication to be applicable in
6G, including the corresponding superheterodyne transmission, modulators,
channel models, channel estimation, beamforming and beam tracking, as well as
signal generation and detection, antenna array manufacturing and efficiency,
among others.

Moreover, as a key driving force of the global Internet, the optical fiber
network connects all continents to form a modern communication backbone
network, providing high-speed data access for metropolises, cities, and towns.
In some scenarios, it might be a promising option to extend optical fiber
transmission directly to wireless interfaces to realize the connection and
mobile access of the last mile. On the other hand, the visible light spectrum
can be employed in LiDAR or other similar forms to sense the environment so as
to assist radio communications, which is a popular use case of JCS.

###  XI-B Joint Communication and Sensing

JCS is a prospective field for 6G, which aims to realize the coordination of
communication and sensing via software and hardware resource sharing or
information sharing [234]. On one hand, in the beyond-5G era, the high
frequency band used by wireless communication networks and the spectrum for
radio sensing gradually approach or even overlap with each other. On the other
hand, the communication system and the sensing system have similar features in
terms of RF transceiver, channel characteristics, and signal processing, which
hastens the research and development of JCS network architecture and related
technologies. Moreover, the development of communication technologies, such as
extreme large-scale arrays, large bandwidths, RISs, and AI, will further
promote the growth of sensing technologies.

In the narrow sense, sensing refers to target positioning (including ranging,
velocity measurement, and angle measurement), imaging, detection, tracking,
and recognition. In the broad sense, the sensing target may be services,
networks, terminals, as well as attributes and states of environmental
objects. JCS can be based upon the same set of equipment and/or the same
spectrum, and is able to lower the equipment cost, size, and power
consumption, and improve spectral efficiency, hardware resource utilization,
and information processing efficiency.

The development of JCS may face multi-perspective multi-level technical
challenges, among which a few major challenges are listed below.

  * •

Fundamental theory for JCS: Different from the classical Shannon information
theory, sensing will give rise to new performance metrics and limits for the
system, based on which new JCS information theory needs to be established to
investigate the optimal performance limits and tradeoff of the two
functionalities.

  * •

Signal processing for JCS: This mainly embodies aspects such as joint waveform
design, joint transmission beamforming, and joint signal reception. From the
viewpoint of functionality priority, joint signal processing can be divided
into sensing-centric design, communication-centric design, and joint weighted
design.

  * •

Protocol and system architecture design for JCS: Communication and sensing may
have different work mechanisms. Taking radar sensing as an example, radar is
generally divided into pulse type and continuous-wave methods, while
communication adopts TDD or FDD. Thus, new transmission protocol and system
architecture are in need to realize the coordinated operation of the
communication and sensing.

The development of JCS is likely to undergo three stages, namely, coexistence,
mutual assistance, and mutual benefit. For coexistence, the originally
independent communication system and sensing system are integrated onto the
same physical platform, where communication and sensing coexist as two service
forms with the main focus on interference management, resource allocation, and
spectrum sharing. In the mutual assistance stage, communication capability and
sensing capability cooperate and aid each other based on shared information,
so as to realize sensing-assisted communication or communication-assisted
sensing, where the main focus lies in air interface design, waveform design,
along with transmission and reception processing algorithms. Ultimately, in
the most advanced stage, communication and sensing will achieve comprehensive
and multi-level integration in spectrum resource, hardware equipment, waveform
design, signal processing, protocol interface, networking collaboration, among
other aspects. Besides the evolution of the existing techniques in the
previous two stages, AI-enabled approaches and multi-cell coordinated sensing
methods will be explored, in order to build the endogenous sensing ability of
6G.

###  XI-C Hyper-distributed, Innovative Services

Mission- and time-critical services will be essential to the further
development of our societies and economy. Such services, like connected
healthcare, autonomous transportation, Industry 4.0, smart grids, smart cities
and homes, have strict QoS requirements, which has led to new decentralized
multi-tier network-computing platforms where processing capabilities are
brought closer to the data sources and/or to the end consumers. This multi-
tier continuum, usually referred to as device-edge-cloud continuum, thus
describes integrated network-computing platforms that consist of
interconnected IoT/mobile devices, edge nodes, fog nodes, and cloud back-end
servers. In these platforms, computing resources and services are seamlessly
combined along the data path from the IoT/mobile devices to the back-end
clouds, passing through intermediary edge and fog nodes.

Most of the aforementioned mission- and time-critical services leverage ML for
data analytics or decision making, which makes it mandatory to support ML
models, and the data pipelines to feed them, in a sustainable manner. This
concerns both the training of such models, whose computing (and energy)
resource requirements may be very high [235], as well as their execution for
inference purposes, especially when capability-constrained IoT/mobile devices
are used.

Additionally, it is worth remarking that among the most relevant services are
media applications, which represent over 80% of the total Internet traffic.
This suggests that human users’ quality of experience should play a central