# Wireless Backhaul in 5G and Beyond:
Issues, Challenges and Opportunities

Berke Tezergil, Ertan Onur B. Tezergil and E. Onur are with the Department of
Computer Engineering, Middle East Technical University, Turkey, e-mail:
<EMAIL_ADDRESS>preprint of this paper can be accessed
at arXiv:2103.08234 [cs.NI]

###### Abstract

With the introduction of new technologies such as Unmanned Aerial Vehicle
(UAV), High Altitude Platform Station (HAPS), Millimeter Wave
(mmWave)frequencies, Massive Multiple-Input Multiple-Output (mMIMO), and
beamforming, wireless backhaul is expected to be an integral part of the 5G
networks. While this concept is nothing new, it was shortcoming in terms of
performance compared to the fiber backhauling. However, with these new
technologies, fiber is no longer the foremost technology for backhauling. With
the projected densification of networks, wireless backhaul has become
mandatory to use. There are still challenges to be tackled if wireless
backhaul is to be used efficiently. Resource allocation, deployment,
scheduling, power management and energy efficiency are some of these problems.
Wireless backhaul also acts as an enabler for new technologies and improves
some of the existing ones significantly. To name a few, rural connectivity,
satellite communication, and mobile edge computing are some concepts for which
wireless backhauling acts as an enabler. Small cell usage with wireless
backhaul presents different security challenges. Governing bodies of cellular
networks have standardization efforts going on especially for the Integrated
Acces- Backhaul (IAB)concept, and this is briefly mentioned. Finally, wireless
backhaul is also projected to be an important part of the beyond 5G networks,
and newly developed concepts such as cell-free networking, ultra-massive MIMO,
and extremely dense network show this trend as well. In this survey, we
present the aforementioned issues, challenges, opportunities, and applications
of wireless backhaul in 5G, while briefly mentioning concepts related to
wireless backhaul beyond 5G alongside with security and standardization
issues.

###### Index Terms:

Mobile Networks, 5G and Beyond, Wireless Backhaul

## Acronyms

5G

    Fifth Generation
6G

    Sixth Generation
AP

    Access Point
AWGN

    Additive White Gaussian Noise
BER

    Bit-Error Rate
BS

    Base Station
BSC

    Base Station Controller
BTS

    Base Transceiver Station
CapEx

    Capital Expenditure
CoMP

    Coordinated Multi-Point
CP

    Content Provider
CSI

    Channel State Information
CU

    Centralized Unit
D2D

    Device to Device
DSL

    Digital Subscriber Line
DU

    Distributed Unit
eMBB

    Enhanced Mobile Broadband
eNB

    Evolved Node-B
FDD

    Frequency Division Duplexing
FSO

    Free Space Optics
GEO

    Geostationary Orbit
gNB

    Next Generation Node-B
GPRS

    General Packet Radio Service
GSM

    Global System for Mobile Communications
GSMA

    GSM Association
HAPS

    High Altitude Platform Station
H-CRAN

    Heterogeneous Cloud Radio Access Network
HetNet

    Heterogeneous Network
IAB

    Integrated Acces- Backhaul
IBFD

    In-band Full-Duplex
IRS

    Intelligent Reflecting Surfaces
KPI

    Key Performance Indicator
LEO

    Low Earth Orbit
LoS

    Line of Sight
LTE

    Long Term Evolution
MBS

    Macro Base Station
MEC

    Mobile Edge Computing
MEO

    Medium Earth Orbit
MIDO

    Multiple-Input Distributed-Output
MIMO

    Multiple-Input Multiple-Output
MINLP

    Mixed-Integer Nonlinear Programming
mMIMO

    Massive Multiple-Input Multiple-Output
mMTC

    Massive Machine Type Communication
mmWave

    Millimeter Wave
MNO

    Mobile Network Operator
MSC

    Mobile Switching Center
NFV

    Network Function Virtualization
NLoS

    Non-Line of Sight
NOMA

    Non-Orthogonal Multiple Access
NR

    New Radio
OAB

    Orthogonal Access & Backhaul
OBFD

    Out-of-band Full-Duplex
OFDM

    Orthogonal Frequency Division Multiplexing
OpEx

    Operating Expenses
PON

    Passive Optical Networks
PtMP

    Point-to-Multipoint
PtP

    Point-to-Point
QAM

    Quadrature Amplitude Modulation
QoS

    Quality of Service
RA

    Resource Allocation
ROI

    Return of Investments
RRM

    Radio Resource Management
SAGIN

    Space-Air-Ground Integrated Network
SDN

    Software Defined Network
SINR

    Signal-to-Interference+Noise Ratio
TDD

    Time Division Duplexing
TDMA

    Time Division Multiple Access
UA

    User Association
UAV

    Unmanned Aerial Vehicle
UE

    User Equipment
UMTS

    Universal Mobile Telecommunications System
URLLC

    Ultra Reliable Low Latency Communication
VNF

    Virtual Network Function
VSAT

    Very Small Aperture Terminal

##  I Introduction

Fifth generation (5G) networks offer 1000x more capacity and 10-100x data
rates compared to the Long Term Evolution (LTE)-Advanced [1]. This will be
possible with the introduction of mmWave frequencies, massive MIMO systems,
directional beams and ultra-dense network structures, to name a few. With the
introduction of these new technologies and concepts, wireless backhauling has
become a prominent figure in future networks enabling densification with high
performance than previous approaches.

We define wireless backhauling as the use of wireless connection for core
network connectivity for which fiber cables are traditionally used.
Effectively, for MBS and SBS’s, employing wireless backhaul means wireless
connectivity in both downlink and uplink connections. Wireless backhauling is
not a new concept; it was used in pre-5G networks by microwave connections or
relays. In LTE-Advanced, self-backhauling concept was introduced enabling the
backhauling of traffic using the LTE-Advanced radio resources.

While the concepts are defined in the previous generations, relays and self-
backhauled cells did not see widespread use. This is mostly because its
characteristic challenges (inter-cell interference, scheduling) were not fully
solved. Furthermore, since radio link is a very scarce resource for networks
using sub-6 GHz frequencies, most network operators did not want to divide
their already strained spectral resources. Another point is that the projected
densification did not take place until now, which allowed the traditional
network structure with MBS’s and User Equipments’ UE to persist. Also, sub-6
GHz frequencies did not meet the performance of fiber cables in terms of data
transfer, which was a limiting factor.

However, with the introduction of mmWave frequencies, mMIMO and beamforming,
the performance requirements ceased to be a limiting factor. Moreover, mmWave
being a new frequency band eased the frequency resources in disposal,
faciliating wireless backhauling. Combined with the simultaneous connection
targets of 1 million per square kilometer for mMTC use-case [2], network
densification became a necessity and a new network architecture was required.
At this point, small cells came into play. Dense deployments of small cells,
whose transmission power are lower than that of the macro cells [3], can
tackle the problem of high connection volume. It is almost impossible to
deploy a dense network with fiber connectivity, which necessitates the use of
wireless backhauling, and hence its significance.

Even though beamforming with mMIMO solves the inter-cell interference problem,
there are still significant problems that do not allow the maximum gains from
wireless backhaul use. Resource allocation and user association is a problem
in wireless backhauling dramatically affecting the overall performance. With
the high number of SBS’s, deployment is an important problem which allows
massive gains from the beginning if solved correctly. Scheduling of access and
backhaul traffic is still a persisting problem, and with the dense networks,
its solution is even more critical to the network performance. With the green
initiatives gaining momentum, reducing power consumption is not only important
for the climate, but it also increases the overall performance of the network.
Finally, wireless backhauling usage enables coverage improvements if the
network is configured for that purpose. These challenges sometimes have
conflicting repercussions, making research in this area laborious but
rewarding.

Finally, if wireless backhaul usage can be widely realized, there are various
use-cases that can significantly gain from it. The usage of UAV/HAPSs and
satellite backhauling are directly dependent on wireless backhauling
performance. If wireless backhauling can enable these, then the solution to
rural connectivity is within reach.

###  I-A Scope

We present a comprehensive overview of pre-5G wireless backhauling
developments. Then, we introduce the concepts related to the wireless
backhauling developments in 5G. Later, we present the challenges related to
wireless backhaul in 5G. We also mention the opportunities made possible by
wireless backhauling, and some applications of it. We briefly mention the
security aspects of wireless backhauling and standardization efforts on it.
Finally, since the research about this topic moves on to beyond-5G, we present
the works on beyond-5G and 6G networks that may be linked with wireless
backhauling.

###  I-B Motivation

We have chosen the topic of wireless backhaul since we believe that it will be
an integral part of future networks. With the introduction of mobile/nomadic
base stations, UAV, and HAPS, wireless backhaul use will be mandatory due to
aforementioned platforms’ nature. Moreover, the IAB concept is envisioned as a
new breakthrough that will change how future networks will operate.

Because of the importance of this topic, there are many works that touch on
this issue from one perspective or another. However, there are no works we
know of that comparatively gives all the available perspectives of this topic.
Furthermore, the surveys in this area are mostly outdated. We wanted to
present a comprehensive work on this concept covering all the angles, be it
the enabled technologies, future use and current status.

###  I-C Difference from Other Surveys and Our Contributions

There are some previous survey papers on similar topics. For backhauling, in
[4], a discussion is made about the wireless backhaul in 5G from different
perspectives, such as the evolution of the cellular network prior to this
idea, the state-of-the-art that enables this concept, challenges that can be
solved and available configurations that can enable the use of wireless
backhaul. In [5], the existing backhaul solutions in terms of different
frequencies are analyzed thoroughly, along with their benefits, limitations
and applications. Moreover, small cells and their backhauling is also
considered from different perspectives such as deployment, flexibility,
interference management, delay management and signaling overhead. An argument
about the usage of half-duplexing and full-duplexing is also made. In [6],
Bojic et al. analyze the wireless backhaul of small cells. Wireless and
optical mobile backhaul technologies are comparatively analyzed. Wang, Hossain
and Bhargava have explored the small cell backhauling in 5G in [7]. General
aspects of the backhauling are explored and some existing approaches and
technologies are briefly analyzed in this work. In [8], Ahamed and Faruque
provide a general image of 5G backhaul, with strengths and weaknesses of every
alternative such as wired (e.g. fiber) and wireless (e.g. mmWave or sub-6 GHz)
backhaul. Polese et al. explain in detail the integrated access and backhaul
concept, its internals such as architecture, network procedures, and
scheduling, and through simulations, evaluated the end to end performance of
an IAB mmWave network with different scenarios [9]. Kamel, Hamouda and Youssef
present the concept of ultra-dense networks [10]. Kamel et al. discuss the
modeling and performance of UDNs alongside the enabling technologies.

For moving networks, UAVs, and satellites, Jaffry et al. explore the moving
networks concept thoroughly in their work [11]. This work is also up-to-date
and covers the concepts related to mobile 5G systems. However, the scope of
this work is limited only to land-based moving networks based on public
transport vehicles. They analyze how these moving networks will improve the
performance metrics and give information about the use-cases and applications
of moving networks. Kurt et al. focus on the high altitude platform stations
and highlight HAPS’ potential on many use-cases [12]. The authors mention how
reconfigurable smart surfaces can be used alogside HAPS’ and present a
detailed overview of radio resource management, handover management, machine
learning for HAPS’, and latest developments in energy and payload systems. Dao
et al. present an overview of aerial radio access networks (ARAN) in [13]. Dao
et al. describe the system model of ARANs and analyze its transmission
propagation, energy consumption, latency, and mobility. Kodheli et al. present
the main innovation drivers, applications and challenges of satellite
communications [14]. Liu et al. review the space-air-ground integrated network
[15]. Liu et al. discuss the network design, resource allocation, performance
analysis, and technology challenges of SAGINs.

Our survey briefly introduces the backhaul concept and wireless backhauling.
Then, we detail the concept that forms the basis for wireless backhauling
research and highlight the crucial topics in this research area. Later, we
introduce the areas that wireless backhaul can serve as an enabler. We briefly
mention the security and standardization issues about wireless backhauling and
finish with beyond 5G vision of the wireless backhauling. To our knowledge,
there exist no surveys that are this comprehensive and touch on multiple
issues as we do. The surveys mentioned above are detailed in one or more parts
that we cover, but they are not as comprehensive as ours are in terms of the
aforementioned perspectives.

###  I-D Outline of the Survey

The rest of the paper is organized as follows: In Section II, we define the
backhaul concept and give brief information on its evolution in cellular
networks, in Section III, we highlight the importance of wireless backhaul in
5G as an enabler to various functions, in Section IV we give the current state
of wireless backhaul and the concepts that are prevalent in its research, in
Section V we introduce the main part of the survey, in Sections VI-XI we
present the challenges of wireless backhauling, in Section XII we show the
applications for which the wireless backhaul concept serves as an enabler, in
Section XIII we give brief information about the standardization efforts done
on the wireless backhaul, in Section VIII we analyze the security aspects of
the wireless backhaul, in Section XIV we briefly mention the evolution of
wireless backhaul beyond 5G, and conclude the paper in Section XV.

##  II The Backhaul Concept and Its Evolution

###  II-A An Overview of pre-5G Cellular Networks

In the first 2G cellular networks, the mobile devices connected to the base
transceiver stations (BTS) wirelessly. These BTS’s were then connected to the
base station controllers (BSC), which had control plane functionality for the
air interface, and a connection to the core network via mobile switching
centers (MSC). All of this connection except the one between the mobile
devices and BTS’s were wired E-1 based or IP-based connections. Initially, GSM
networks had the task of carrying voice but with the introduction of GPRS, GSM
networks also started carrying data. The voice used circuit-switching, which
meant that required resources for the call were allocated on the call setup
and then released after the call. On the contrary, data was transported on the
Internet using packet switching, meaning that 2G networks had to implement and
use both stacks on their network elements.

In 3G, base stations of UMTS (which are called Node-B’s) were connected with
high-speed DSL connections, microwave radio, fiber and microwave Ethernet
lines. Moreover, the transition was to the IP-based protocols instead of
legacy SS7. Like its predecessor, 3G networks also carried voice and data
using the same infrastructure. This duality imposed more and more challenges
in network design and operation. For example, any network element had to
implement one stack for voice traffic with its own protocols and another one
for data traffic. Moreover, scheduling and handling two different stacks added
complexity to network management and operation. Because of these reasons,
especially during 3G, the transition from circuit-switching to packet-
switching became very common due to the increasing effect of data services
over the whole network. With a packet-switched network, all services can be
implemented on a unified core, resulting in better resource utilization and
improved cost efficiency [16].

Despite the difference in voice and data service, networks usually used the
same backhaul lines to the controllers and to the core network. The ATM-based
transports provided constant throughput, which was suitable for the voice
traffic but not for data traffic since it had a bursty nature. Ethernet was a
technology that has proven itself time and again for the data communications.
In spite of this reliability, Ethernet could not be used directly in the
telecommunication networks due to some inherent differences, such as the lack
of synchronization signals, even though it delivered higher capacity at a
lower cost per bit [17]. Finally, with the introduction of Carrier Ethernet,
these shortcomings were solved and operators were able to unify the voice and
data communication on the same network without any additional supporting
protocols [16].

With the introduction of LTE, the network adopted an all-IP approach, which
finally unified the voice and data duality. This unification is realized by
transitioning the voice telephony to the IP side. Network design was also
greatly simplified with this unification, as the network now implemented and
operated only one stack. Perhaps the biggest advantage of IP architecture was
that all interfaces became IP compliant, and physical infrastructure became
transparent and interchangeable. These developments made it possible to change
the backhaul medium quite easily and opened new possibilities to implement
wireless backhauling. To give an example, using the same interface for all
connections effectively meant that theoretically, a BS could connect to
another BS since both used the same protocols and procedures. In the next
subsection, we will detail the wireless backhaul usage for the mentioned
networks.

Figure 1: A 5G cellular network employing different technologies for
backhauling.

###  II-B Wireless Backhaul in pre-5G Networks

For GSM/GPRS networks, only wireless backhaul solution was microwave radio at
this time. Although backhauling using satellite was possible, it was too
expensive and cumbersome to be of wide use. At that time, passive optical
networks (PON) and mmWave radio were seen as the future backhaul technologies
enabling higher datarates with better cost-efficiency [16]. However, while the
mmWave technology has the potential to support Gbps-level speeds, it was not
fit for cellular use due to its power consumption and hardware characteristics
up until 5G era [18].

For LTE and LTE-Advanced networks, relays were introduced as a new network
element to improve network coverage and range by employing wireless
backhauling. 3GPP TR 36.806 [19] detailed the relay architectures. There were
two types of relays: Type 1 relays had cells and identities of their own and
had the same radio resource management (RRM) mechanisms as the eNBs, Type 2
relays did not have identities and some parts of the RRM was controlled by
their donor eNB [20]. Type 2 relays supported L1 (decode-and-forward) and L2
relaying, which did not require the implementation of the whole RRM stack and
consequently made these type relays simpler. Type 1 relays also supported L3
relaying and even self-backhauling, which we will mention later.

Both Type 1 and Type 2 relays were dependent on donor eNBs for their
operations, and while Type 1 was capable of independent operations with a
unique identity, it was still classified as a relay, not as an eNB with
wireless backhaul capabilities. This shortcoming also shows the expectation of
the network maintainers from relays: they were seen as a way to extend network
coverage. This is more evident in the later version of the relays in Release
11 where their operation scenarios were defined as coverage extension and
indoor coverage [21].

While the proposed concepts did have some good points and were a potential
game-changer for contemporary networks, LTE relays did not see widespread use.
There are two major reasons for this: the first reason is that in pre-5G
networks, frequency was a very scarce resource. LTE networks were stuck to
sub-6 GHz frequencies for access, and this weakened the LTE relay concept
significantly simply because using the same frequencies for backhaul meant
taking away from the access side which was already strained. The second reason
is that the networks did not meet the densification projections that were made
for LTE networks. In a sense, the networks did not get dense enough to justify
the deployment of wireless relays and allocation of spectrum from access to
backhaul side.

To make eNB’s capable of wireless backhauling, self-backhauling concept was
introduced in LTE-Advanced. This new concept gave eNB’s full relay capability:
in essence, self-backhauling enabled eNB’s to use the same radio resources for
both access and backhaul usage. Self-backhauling improved spectrum efficiency
through resource reuse, and cost efficiency through hardware and management
tools reuse. However, the self-backhaul brought about a few challenges of its
own, such as access-backhaul interference, and scheduling between access and
backhaul [22]. Gamboa and Demirkol [23] have experimented using the LTE self-
backhauling solution and have shown improvements in both the coverage and
downlink bitrate of the network, despite self-interference and in-band
communication reducing the effectiveness of the overall setup.

The challenges introduced with self-backhauling and relays will be handled
with the new technology introduced in 5G. Bhushan et al. highlight network
densification as the key mechanism for future wireless evolutions [24]. This
densification is twofold: spatial densification and spectral aggregation. The
spatial densification is achieved by increasing the number of antennas per
node as well as increasing density of base stations deployed in a given area.
The spectral aggregation is achieved when larger amounts of bandwidths are
used. However, these concepts are beneficial only if the backhaul can support
the denser network. The details of these developments and challenges will be
given in the next section.

##  III Importance of Wireless Backhaul in 5G

In the previous section, we mentioned some concepts introduced for wireless
backhauling, namely relays and self-backhauling. In this section, we will
first mention the network densification concept that goes hand in hand with
wireless backhauling. We will mention why the densification is required and
the challenges related with realizing it.

With wireless backhauling, a resource allocation problem is also introduced
since access and backhaul effectively uses the same wireless resources. For
this problem, fixed access & backhaul, and integrated access & backhaul
concepts are introduced and we will mention them later in this section.

###  III-A Network Densification

The three major use-cases of the 5G, namely Enhanced Mobile Broadband (eMBB),
Ultra Reliable Low Latency Communication (URLLC)and mMTC, has shaped the 5G
network architecture. These three use-cases necessitate the development of
different technologies and concepts to meet the service requirements,
sometimes with conflicting targets. The network architecture has become
increasingly denser over the years, from 4-5 BS/km2 in 3G to an anticipated
40-50 BS/km2 in 5G [25]. This densification cannot be achieved with the
existing macrocell architecture, and small cell architecture is introduced to
realize the densification.

There are some initial challenges related to the densification. First
challenge is the cost of deployment for SBSs. Fiber backhauling solutions may
be effective in terms of the connection quality it provides, but not only it
is costly, but installing fiber is also a tedious process which takes time and
effort. Moreover, there may be some infrastructural challenges for cabling
certain areas, such as roads or densely populated urban centers. Costs can be
significantly reduced by employing wireless backhauling on small cells as they
do not require any extra infrastructure. Furthermore, since wireless access
and backhaul can use the same medium and protocols, there is further cost
reduction through hardware reuse.

Second challenge related to the densification is the performance.
Densification can only be realized with wireless backhauling, but the
performance of wireless backhauling with sub-6 GHz frequencies is nowhere near
fiber. In wireless backhaul, a new type of interference is introduced into the
system: namely the access-backhaul interference, which reduces the performance
of both links. In releases 10 and 11, enhanced inter-cell interference
coordination (eICIC) and further enhanced inter-cell interference coordination
(feICIC) were introduced to reduce interference in order to extend the
coverage range in heterogeneous networks (HetNets) [26, 27, 28]. Traditional
fiber backhaul solutions do not suffer from any interference problems and
perform adequately well in any situation.

Even if the interference problem is solved, the spectrum used in LTE is not in
a capacity to satisfy the requirements of 5G networks. With the introduction
of mmWave spectrum in Release 15, the wireless backhaul finally has the
potential to reach the performance of its fiber counterpart. With the beam-
based air communication in mmWave spectra, the access-backhaul interference no
longer becomes the limiting problem, as they are noise-limited unlike the
interference-limited LTE networks [29].

###  III-B Access & Backhaul Resource Management

With wired backhauling, BSs manage the air interface for effective user
access. The resources are allocated in such a way to maximize the users’
throughput. However, if a BS employs wireless backhauling, then the air
interface management has to balance users trying to access to the network and
the BS trying to send the backhaul traffic to network core. In other words,
available resources have to be allocated between the access and backhaul
sides.

Initially, two kinds of resource management solutions existed: namely fixed
access & backhaul and IAB. In fixed access&backhaul, the resources that will
be used for access and backhaul are separately fixed beforehand [30]. Although
the solution is viable in the wireless backhaul sense, it leaves a
considerable room for improvement in some specific cases. For example,
especially during rush hours, some SBSs will receive considerably more
connections than others, which will strain the access resources that these
SBSs have. In this case, if would be much more beneficial for the SBSs to
channel the unused backhaul resources to the access side as to increase the
service quality. On the other hand, when SBSs stay idle due to the number of
connected UEs (or lack thereof), the unused access resources can actually be
used in the backhauling of other SBSs which experience heavier loads.

IAB is proposed to replace fixed access & backhaul, for it allows a flexible
split of access and backhaul resources to increase the overall efficiency of
the network [31]. Moreover, another advantage of the IAB is that both access
side and backhaul side will use exactly the same hardware and technology for
their purposes, cutting off hardware costs significantly, while eliminating
fiber cabling costs altogether due to employment of wireless backhauling.
Specifically, IAB has also other use cases other than supply wireless
backhaul, such as bridging the indoor-outdoor coverage, enhancing the system
capacity and extending the coverage [32].

Due to such advantages, IAB is seen as an enabler to the deployments in FR2
spectrum (24-52 GHz). Aside from being an enabler, IAB also adds reliability
and adaptability to the system in cases of unit failure (since an alternative
path can be found on the fly), transmission multiplexing capabilities since it
is designed to operate at much higher frequencies than LTE (400 MHz per
component carrier as a result of this), and multihop transmissions to extend
the coverage of gNB’s [33].

3GPP has also prioritized the IAB concept as the enabler for ultra-dense
networks, and there is a study item prepared for this topic. This will be
mentioned later in Section VII.

Even though the introduction of new technologies have made wireless
backhauling or IAB more efficient, there are new problems introduced alongside
these new improvements, as well as existing problems that are not fully
solved. These must be handled accordingly if network operators are to use IAB
in their networks extensively. These concepts and challenges will be mentioned
later in Sections V-A-V-F.

##  IV Current State of Technologies Related to Wireless Backhaul in 5G

In this section, we will analyze some aspects of the wireless backhaul
technologies that are currently in use. We will explore the frequencies,
topologies, and frequency use schemes. Moreover, we will also detail some of
the widely used concepts that can be considered an enabler to effective
wireless backhaul, such as MIMO.

We have chosen to elaborate on these topics before delving into the literature
since these topics represent general characterstics of wireless backhaul.
These concepts model the characteristics and design considerations to realize
wireless backhauling; whereas the literature focuses on the actual problems
such as resource allocation or user association. We find it necessary to get
through these aspects to understand the research on wireless backhauling.

We will mention the frequencies used widely, from sub-6 GHz to sub-THz
frequencies. We will introduce the topologies that wireless backhaul networks
widely use, and the effects of different topologies on wireless backhauling.
We will mention MIMO, how it works, and why it is important for wireless
backhauling. Finally, we will introduce the half-duplex and full-duplex
communication modes.

###  IV-A Frequencies

In Release 15 New Radio (NR), the air interface is expected to perform much
better than LTE-Advanced even with the same frequencies. NR supports maximum
mobility of 500 km/h (from 350 km/h) while having ten times better minimum
latency. Moreover, peak data rates without carrier aggregation for LTE-
Advanced are 600 MB/s for downlink and 300 MB/s for uplink, whereas for NR
these values are 4.9 GB/s and 2.4 GB/s, respectively. These values for NR are
for the FR1 frequencies, which lie between 450 MHz and 7.125 GHz. Suppose we
consider the FR2, whose frequencies lie between 24.25 and 52.6 GHz and which
allows four times better maximum bandwidth & minimum subcarrier spacing and
two times better maximum subcarrier spacing. In that case, the aforementioned
figures go to 10.7 GB/s and 4 GB/s, respectively [33]. These figures alone
show the effect of higher frequencies on the performance.

FR2 is the first frequency band above the traditional sub-6 GHz band. Above
that, there are also various other alternatives such as V-band (57-64 and
64-71 GHz), E-band (71-76 and 81-86 GHz), and D-band (110-170 GHz). From a
theoretical point of view, these bands carry even better value than the FR2
due do their higher frequencies, but their use is not without challenges. For
example, V-band suffers from severe oxygen absorption, E-band is affected by
heavy rainfall, and D-band has relatively high atmospheric attenuation [34].

It needs to be highlighted that this desire to migrate to higher frequencies
came out of necessity as the lower frequencies are highly congested and cannot
meet the demands of 5G networks due to their characteristics. At microwave
frequencies, higher capacities came thanks to complex radio frequency
techniques such as multipath propagation, channel aggregation, and cross-
channel polarization. In contrast, higher frequencies deliver high throughput
due to their generous spectrum [6]. Fig. 2 highlights the spectra that are
considered for 5G and beyond. An overall overview of the frequencies mentioned
here can be found in Table I.

Before further elaboration of different frequencies, we have to clarify the
licensing approaches for frequency usage as this directly changes their appeal
to network operators. There are three approaches for licensing. The first
approach is fully licensing a spectrum. This is the most common licensing
scheme, especially for sub-6 GHz frequencies. In this usage, the network
operator buys a share of spectrum for exclusive use. The exclusive use allows
the operator to operate interference-free.

The second licensing scheme is unlicensed use or general authorization. The
advantages of this scheme are no payment for frequency use and extra spectrum
availability. However, since there is no exclusive use, interference can
hamper the performance [1]. Furthermore, potentially this can be an unsolvable
problem since with this kind of licensing, none of the users can claim
rightful use. WiFi bands of 2.4 and 5GHz are also examples of usage adhering
to this scheme. These frequencies are not used for wireless backhaul since
these are not considered stable enough to be used for backhauling. Moreover,
these bands are not predictable in the sense of characteristics such as jitter
and latency can have large variations.

For 60 GHz frequencies, coordination of transmit beams and channel access are
centralized methods that reduce the effects of problems arising in unlicensed
spectrum. These solutions can make these frequencies suitable for wireless
backhauling. However, these are not adequate for multi-radio access
technologies and distributed approaches are required. This unlicensed
frequency range is expected to be standardized in NR Release 17 and beyond
[35], which can pave the way for wireless backhaul usage as well.

The last licensing scheme is light licensing. This is a new approach developed
with the introduction of mmWave frequencies. While there are no solid
definitions for this licensing scheme, this one provides more flexibility and
less constraint for the frequency usage, while providing some semblance of
protection. Light licensing is anticipated to be used for frequency bands
where the interference risk is low [36]. mmWave is such a frequency band due
to its propagation characteristics and use of highly directive wave forms in
communication.

Light licensing is also implemented differently in various regions. For
example, in the United States, an automated interference analysis and
verification of compliance to specific rules is made. On the contrary, in the
UK, the licensee is responsible for interference analysis, reducing the
regulator’s role to a minimum [36]. As can be seen, light licensing is
understood and implemented differently in different countries.

Figure 2: 5G spectrum from research and standardization viewpoints can be seen
in the figure alongside the characteristics of different bands.

####  IV-A1 Microwave Frequencies (Sub-40 GHz)

LTE systems heavily used the sub-6 GHz and higher microwave frequencies for
their backhaul purposes. With developments in processing and signaling
technologies, these frequencies managed to keep up with the data demand of
pre-5G networks. Sub-6 GHz frequencies can meet throughput demands of up to
500-750 Mbps, while higher microwave frequencies (7-40 GHz) can exceed 1 Gbps.
The main difference is that sub-6 GHz frequencies can operate in non-line of
sight (NLoS) mode while higher frequencies require line of sight (LoS) for
seamless operation. The reason for this different operation is the directivity
of higher frequencies; highly directive beams are used for communication,
which is interrupted when any obstacle gets in the beam’s way

Initial mMIMO and ultra-dense developments used 26-28 GHz frequencies as an
enabler to get higher rates. For example, [37] used 28 GHz frequencies for
small cell deployments in different configurations. Similarly, [38] used the
same frequencies for point-to-point in-band backhauling. Moreover, this
frequency range is in wide use for backhauling. Throughout the world,
different bands in this frequency range are in use for wireless backhaul [39].
As given above, this frequency band is the first one that can be exploited to
meet the metrics during the initial deployment of 5G networks.

$K_{a}$-(26.5-40 GHz in IEEE definition) and $K_{u}$-bands (12-18 GHz) also
lie in this range. These two bands are widely used in satellite
communications. $K_{a}$-band is used for the satellite-terrestrial backhaul
communication, and numerous literature about the topic can be found [40, 41,
42, 43]. Similarly, $K_{u}$-band is used for the forward links (space to
earth) in satellite communications [44].

####  IV-A2 V-band (57-64 and 64-71 GHz)

V-band consists of two sections of the spectrum whose use is not linked to any
licensing. Numerous countries such as United States, Canada, and Australia
have allocated a band between 57 and 64 GHz, making this band a common one
throughout the world. These frequencies are set aside for unlicensed outdoor
usages, which is not expected to suffer from interference as the connections
are LoS and point-to-point. Moreover, both V-band and E-band are inherently
resistant to outside attacks since the signal’s interception will entirely
block the communication, which will almost surely be notified by the network
operators. This frequency range is well suited to high-capacity, short hops,
making them an ideal candidate for wireless small cell backhaul. V-band
potentially offers throughputs of up to 10 Gbps with a range of up to 1 km
[39].

The first shortcoming of V-band is that it suffers from severe oxygen or dry
air absorption, limiting its range considerably. Furthermore, V-band
communication relies on LoS since the beams cannot penetrate foliage or
reflect from other surfaces [34]. Finally, since V-band is unlicensed, the
vendor interest is not as high as other bands [39].

####  IV-A3 E-band (71-76 and 81-86 GHz)

E-band consists of two spectra that are lightly licensed. Contrary to the
V-band, E-band is very lightly affected by the atmospheric attenuation [34].
This band is also resistant to interference like other mmWave frequencies as
the communication relies on highly directive narrow beams. Spatial frequency
reuse is also made possible due to this directivity property. Thanks to wider
channel sizes in these frequencies, a throughput of 25+ Gbps can be reached,
making this band suitable for 5G operations. Its range is also around 3 km,
which is higher than that of the V-band [39].

E-band can be used effectively in different scenarios with the help of
different hardware. In suburban scenarios, E-band can supply up to 10 Gbps
bandwidths up to 5 miles [45]. Whereas in urban scenarios, 10-20 Gbps
bandwidths can be achieved up to 2 miles. This flexibility enables true 5G
densification by addressing different use cases.

While not as severe, E-band suffers from heavy or excessive rainfall, limiting
its range, albeit not as oxygen absorption does to the V-band. Like V-band,
E-band also requires LoS communication as the beams cannot penetrate obstacles
or reflect from them.

####  IV-A4 W-band (92-110 GHz)

W-band is quite similar to the D-band in terms of its characteristics. It
houses multiple bands that span contiguous regions. This results in an
advantage enabling high bandwidth usage. For example, two separate subbands in
this band can supply 5 GHz and 7.5 GHz, respectively [46]. Moreover, W-band
performs slightly better than the D-band in terms of attenuation and range.
However, D-band is considered a better alternative since it houses even wider
subbands, enabling better performance.

####  IV-A5 D-band (110-170 GHz)

D-band is a lightly licensed frequency band consisting of multiple smaller
bands. Since D-band frequencies are higher than that of V-band and E-band, a
higher level of throughput is easier to achieve. This band can also
potentially reach a throughput of 100 Gbit/s with certain configurations [47],
meaning that this band is the ideal candidate for high throughput
requirements. This performance can serve not only small cells, but also make
high-throughput backhauling of macro cells possible. Moreover, D-band allows
contiguous frequency deployments of up to 12.5 GHz, allowing higher channel
sizes along with carrier aggregation [46].

The D-band disadvantages are as follows: D-band suffers from relatively higher
atmospheric attenuation compared to the E-band . Also, appropriate beamforming
usage is a must for these frequencies as the omnidirectional free space path
loss is higher compared to the lower frequency bands due to Friis’ law.
Finally, this frequency band is relatively young compared to the lower
frequency bands, and as such, vendors are not ready to use this band to its
full potential as they do with lower frequency bands [34].

####  IV-A6 Sub-THz frequencies and beyond

Beyond D-band, some research uses free space optics (FSO) technology to
replace fiber in some areas or use FSO in tandem with RF systems. FSO uses
unlicensed frequencies beyond 300 GHz. It has very high bandwidth and supports
full-duplex operation, making FSO an effective medium for wireless
backhauling. However, FSO requires LoS for operation, and is affected by
weather phenomena such as fog and dust storms. Because of such
characteristics, researchers have focused on developing hybrid systems with
FSO and RF, employing the best of both worlds i.e. high-capacity and low-
latency of FSO and low-cost, high-coverage and high-availability of
microwave/mmWave RF. Examples of research using FSO can be found in [48, 49,
50, 51]. Other technologies that lie in this frequency range are covered in
Section VIII because the research that concerns them is often labeled as
beyond 5G and/or even 6G.

TABLE I: An overview of the mentioned frequencies that can be employ for wireless backhaul and their characteristics. Wired alternatives are provided for comparison purposes [52]. Frequency |  Range |  Strengths |  Weaknesses
---|---|---|---
Microwave Frequencies |  <40GHz |  exclusive use from licensing, high range, can operate without LoS |  high costs of licensing, suffers from interference, low throughput
V-band |  57-71 GHz |  no license costs, resistant to interference, higher bandwidths available compared to lower bands, quick deployment |  no exclusive use from licensing, requires LoS, severely affected by oxygen absorption, limited range
E-band |  71-76 and 81-86 GHz |  light-licensing, high throughput, resistant to interference, higher range than other mmWave bands, adaptable to different conditions, quick deployment |  requires LoS, severely affected by heavy rainfall, still less range than microwave
W-band |  92-110 GHz |  slightly better than D-band in terms of attenuation and range, high bandwidth, resistant to interference |  smaller contiguous regions available than D-band, short range
D-band |  110-170 GHz |  light-licensing, contiguous regions available for higher channel sizes, high throughput, resistant to interference |  affected by atmospheric attenuation, higher free space path loss compared to lower bands, less mature than lower bands, short range
Sub-THz Frequencies |  170GHz-1THz |  unlicensed usage, quasi-immunity to interference, high bandwidth |  very short range, LoS requirement
Fiber-optic |  - |  ranges up to 80km, quasi-immunity to interference, high throughput |  very long deployment time, higher deployment costs than other wireless alternatives
Copper |  - |  ranges up to 15km, quasi-immunity to interference |  very long deployment time, higher deployment costs than other wireless alternatives, performance not adequate for future deployments

###  IV-B Topology

For wireless backhaul, there are two topologies considered, which are point-
to-point (PtP) and point-to-multipoint (PtMP). In PtP topology, the connection
is typically LoS and highly directional antennas are used for this purpose.
The advantages are that interference is often not a problem due to the
connection’s directivity and the whole bandwidth is available for
transmission. However, as the number of connections increase, hardware costs
increase considerably due to both sides requiring transceivers. Moreover, if
LoS connection is not viable e.g. in parks where foliage is common,
performance suffers considerably. PtP topology is also used frequently with
tree topology and a hierarchical network structure. The root of the tree has
fiber connectivity and data is sent from the leaves until a fiber-connected
base station is found.

PtMP topology, on the other hand, requires only one transceiver, often on the
macro base station, which is linked to the small cells. The bandwidth is
shared among all small cells connected to the transceiver, which usually means
that the throughput of the links will usually be lower than that of the PtP
links. Setup times and number of transceivers required are also less in PtMP,
meaning that the expenditure for setting up such a network is also lower [39].
Figure 3 exemplifies the two topologies. While the number of MBS/SBSs and
their positions are the same, the links are different between two cases. In
PtMP case, there is only one transceiver on the MBS supporting both LoS and
NLoS connections; whereas in the PtMP case, there is one transceiver for every
SBS that the MBS is associated with due to LoS connections.

Figure 3: Topology types and links on a cellular network. PtP case pairs a
single transceiver to a BS, whereas PtMP gives area coverage, reducing the
number of transceivers needed at the MBS.

###  IV-C MIMO

The concept of using multiple antennas for communication is not a new one,
being in commercial use since LTE [53]. However, early MIMO systems made use
of only a few (less than 10) antennas in both BS and UE side [54], and the
technology was mainly employed to make use of multiple propagation paths. In
theory, this concept can also be used to employ spatial multiplexing by
serving different users instead of cooperating to serve a single user. This
concept was initially known as multi-user MIMO [55]. Later on, with the
introduction of antenna arrays that house orders of magnitude more antennas,
this was named massive MIMO in 5G. Massive MIMO is an important concept in
wireless backhauling as it enables effective mmWave usage with multiple small
cells.

In 5G, with the introduction of higher frequencies, using directional antennas
became a necessity for effective communication, as explained in the Section
IV-A. Massive MIMO became an enabler for effective mmWave frequency usage in
MBSs. Massive MIMO employs a large number of small antennas used together to
generate a directional beam. This approach is also known as spatial
multiplexing, because the antenna array can generate multiple beams that use
the same frequency. These beams do not interfere with each other if their
directions are different.

Furthermore, asymptotic arguments establish that as the number of antennas in
an array approach to infinity, under certain conditions enormous gains are
made. The effects of uncorrelated noise and fast fading are nullified. Cell
size stops to limit throughput and number of terminals. Spectral efficiency
becomes independent of bandwidth. Finally, the required transmitted energy per
bit vanishes altogether [54].

Another advantage of massive MIMO systems is their energy efficiency. Compared
to a single-antenna BS, a massive MIMO system can theoretically achieve the
same uplink rates with $\frac{1}{N_{t}}$, where $N_{t}$ is the number of
antennas in the array. If imperfect channel state information (CSI) is
assumed, the power requirement becomes $\frac{1}{\sqrt{N_{t}}}$, which still
corresponds to significant power savings. Alternatively, the energy efficiency
can be employed to extend the range [54].

Massive MIMO systems improve the efficiency in a number of ways. These systems
employ a high count of small amplifiers that are less complex, have low power
usage, and considerably cheaper costs compared to the conventional antennas
with high power usage. Moreover, thanks to the concept of beamforming and
having a large number of antennas, the air latency is substantially reduced
and an inherent strength against jamming(or interference) is achieved. A final
advantage is that with massive MIMO usage, employing OFDM results in a more
efficient air channel, which reduces the physical layer signalling [1]. These
aspects result in improvements from commercial and signaling perspectives.

While the massive MIMO technology is considered as an enabler for mmWave
usage, it can also be used below 6 GHz frequencies. This can be advantageous
since hardware components are more mature for this frequency range. However,
the hardware implementation will surely be different [56].

Massive MIMO helps wireless backhauling in the following ways. First of all,
enabling mmWave usage strengthens the 5G gNB’s in a number of ways. With
denser cell usage, a gNB can serve multiple small cells or UEs with massive
MIMO, employing spatial multiplexing and frequency reuse. With mmWave
frequencies, the performance will also be on par with what is expected in 5G
networks. Furthermore, being able to extend the range with massive MIMO also
helps cell-edge performance, which is an issue in the past networks. We do not
highlight other characteristics of massive MIMO as it requires a detour from
our original topic, but interested readers are encouraged to read [56] where
massive MIMO is explained by clarifying the common misunderstandings.

###  IV-D Half- and Full-duplexing

Due to its medium, wireless communication has to solve the medium access
problem different than wired communication for efficient usage. In wired
communication, this problem is arguably easier to solve since it is easier to
understand whether the medium is accessed or not. If a power spike is detected
on the wire, then it is concluded that someone is accessing the medium. This
cannot be done on a wireless medium in this way and with this level of
reliability. Furthermore, traditional antennas are able to either act as a
receiver via listening the medium, or as a transmitter via generating the
necessary waves. This cannot be done on the same time because an antenna can
only listen to itself (since the generated wave is the closest) while
generating a wave.

The aforementioned communication is half-duplex, since one party can either
transmit or receive in a time instant. This is not an efficient way of
communication since it cuts the potential performance by half during
operation. For example, in a two-party communication, each party can only
assume one role at a time, and if every party has 5 seconds worth of data to
send, it takes 10 seconds in the best case to send the data. In theory, this
communication could be carried out in 5 seconds if both parties were able to
send their data while receiving the other party’s data.

Full-duplex communication is an important concept that can result in enormous
performance gains if it can be implemented correctly. With the introduction of
beamforming and directive communication, full-duplex communication can be
realized with the use of spatial multiplexing. This means that irrespective of
the bands used, a station can receive from one sector while transmitting from
another one. Full-duplex communication can also be done in-band i.e. using the
same frequency. This is harder to implement since opposite directions generate
interference which makes communication difficult. However, as mentioned,
potential performance gains make this research promising.

Full-duplexing is being considered for wireless backhauling with different
settings. There are examples with spatial multiplexing that are arguably
easier to implement thanks to beamforming. In-band full-duplex is harder to
implement because of the interference problem. This is an open research area
important for future wireless communication.

##  V Challenges of Wireless Backhaul

Having mentioned the general concepts that are seen in most papers of wireless
backhauling area, we now present the reader our survey. Further sections will
present the challenges of wireless backhauling, state-of-the-art in these
topics, main learning points, and open issues regarding these areas.

For this purpose, we have highlighted six main topics: resource allocation,
deployment, scheduling, performance evaluation, coverage, and security. Each
of these topics have their own section with the same structure defined in the
previous paragraph. These topics are especially important because their
solutions are directly related to the effective realization of wireless
backhauling. The specific points regarding their importance to the wireless
backhauling are highlighted in the respective sections.

Wireless backhauling is an area that shares many challenges with traditional
networking. Deployment, security, and coverage are examples of such
challenges. Furthermore, there are challenges more specific to wireless
backhauling due to different characteristics, such as scheduling of small cell
backhaul traffic or deployment of small cells for high performance. Resource
allocation seems to be the paramount problem for wireless backhauling as the
dynamic management of resources is required to effectively use the scarce
resources available. Finally, performance evaluation works are significant
since these show the feasibility of wireless backhaul usage in the field
without any performance losses, while also highlighting the necessary
adaptations for effective field usage.

###  V-A Resource Allocation

The first challenge before the implementation of any wireless backhaul system
is the resource allocation problem. Resource in this context can be defined as
anything that the network manages to perform its operation. Examples can be
the bandwidth, energy, links and their capacities, or time-slots. Networks use
these resources to optimize their performance. Frequently, the bandwidth or
energy allocation has to be carefully adjusted to get the best possible
performance from a wireless backhauled network.

User association is another frequently seen aspect of the resource allocation
problems. For HetNets, a UE can get service from multiple BSs at any given
time since SBSs and their associated MBSs often have overlapping coverage
areas. Because of this, user association becomes another decision which can be
used to optimize overall network performance. In this regard, approaches may
be to maximize the average sum rate or the worst performing UE. User
association is also used for load balancing of various resources, meaning that
suboptimal associations may take place. User association is mostly taken into
account with another parameter such as transmit power allocation or spectrum
allocation/scheduling.

In this section, we gather the literature aiming to tackle the resource
allocation problem; the findings are also presented in Table II at the end of
this subsection for readers’ convenience. This section is the longest part of
our survey regarding the wireless backhauling in 5G as the scope of resource
allocation is quite large. Furthermore, some points are shared between the
resource allocation and further sections; for example, some papers tackle the
user association and scheduling problem, which makes them eligible for both
sections in our paper. We mention these works in resource allocation section.

####  V-A1 State of the Art

In [57], Bonfante et al. use massive MIMO to construct a two-layer network in
which small cells use wireless access and backhaul links. The two-layered
system consists of mMIMO-BSs and small cells that use a fixed allocation of
access and backhaul slots. All small cells receive their respective backhaul
traffic from the base stations employing mMIMO. These BSs are connected to the
core network with high-capacity wires and serve only the small cells, whereas
the small cells only serve UEs and do not take part in backhaul of other small
cells’ traffic. For their system, the parameter $\alpha\in[0,1]$ defines how
the available timeslots were divided between access and backhaul usage. Two
different deployment scenarios are considered for small cells. In the first
scenario, the small cells are randomly and uniformly distributed to the BS
area; whereas in the second one, which the authors named as ad-hoc deployment,
they are deliberately placed closer to UEs to increase the throughput.

Tang et al. optimize beamforming and power allocation to maximize energy
efficiency in CoMP networks doing simultaneous wireless information and power
transfer (SWIPT) [58]. There are two types of users in these networks, namely
the energy harvesting users (e.g. IoT devices) and information decoding users
(e.g. laptops, mobile phones). Tang et al. separately consider beamforming and
power allocation since the combined problem is non-convex. The authors first
propose a zero forcing approach for beamforming to suppress interference for
all nodes. The authors also propose a partial zero forcing approach for
beamforming, which exploits interference as an energy source for energy
harvesting users, while at the same time cancelling the interference of
information decoding users. Simulation results show that partial zero forcing
outperforms the zero forcing method. Moreover, both methods converge to their
global optimals after a number of iterations. Increasing the number of small
cells and information decoding users also increases the energy efficiency.
Increasing the number of energy harvesting users increases the energy
efficiency up to a certain number, after which efficiency sharply decreases
for zero forcing, and decreases mildly for partial zero forcing.

In [59], Zhang et al. have formulated a problem to optimize the energy
efficiency of wireless backhaul bandwidth and power allocation problem in a
small cell. The network in question is a two-tiered network in which the MBS
uses mMIMO to communicate with the small cells whereas small cells use OFDM to
communicate with the UEs. The aforementioned problem is formulated as a
nonlinear programming problem, which is shown to be nonconvex. Therefore, the
authors have decomposed the problem into two convex subproblems for wireless
backhaul bandwidth allocation and power allocation. Two versions of the
algorithm are developed, namely the optimum iterative algorithm and low-
complexity algorithm, which fixes the bandwidth allocation factor by the value
calculated from the equal power allocation. It is shown that the proposed
iterative algorithm gets better results in all metrics than the low-complexity
one, whereas they both outperform the existing benchmark.

Nguyen et al. study the joint design of downlink transmit beamforming and
power allocation in two-tier wireless backhauled small-cell heterogeneous
networks [60]. Nguyen et al. first formulate the problem of maximizing the
energy efficiency of the wireless backhauled small cell HetNets. The proposed
power consumption model considers adaptive decoding power at each small cell
access point (SAP), making the model more appropriate. The problem is non-
convex and NP-hard, and Nguyen et al. propose an exhaustive search based on
branch-and-bound algorithm for global optimal solution. However, this solution
has high complexity, and Nguyen et al. also propose a low-complexity algorithm
based on first order Taylor convex approximation (FOTCA). As the final step,
Nguyen et al. also extend the problem formulation to consider the small cell
selection that takes into account the impact of power to switch on/off the
SAPs. Introducing this as a binary variable, the authors model this as a mixed
integer second order cone programming (MISOCP) approximated problem. This
problem is decomposed into subproblems by the ADMM approach and Nguyen et al.
develop a distributed algorithm to solve this problem individually at every
SAP. Numerical results indicate that the all proposed methods outperform the
fixed power allocation scheme. Moreover, the small cell selection algorithm
also outperforms others since selectively activating the SAPs results in
significantly less power consumption. Second best algorithm is the adaptive
decoding power. Finally, it is shown that having a high power budget usually
results in the MBS consuming too much power and reducing the energy
efficiency. Nguyen et al. suggest that having low budgets result in better
energy efficiency since it does not allow excessive power allocation.

In [61], Saha, Afshang and Dhillon explore the bandwidth partition scenarios
between access and backhaul side of the network in a full wireless
configuration. The authors propose a network that used sub-6 GHz frequencies
for control traffic and mmWave for data traffic. For the backhaul traffic of
small cells, Saha et al. compare two different strategies. In the first
strategy, the available bandwidth is partitioned equally to all small cells.
In contrast, in the second strategy, the bandwidth is partitioned proportional
to each small cell load. The authors report that the load-balanced
partitioning always provides higher coverage than fixed partitioning.
Furthermore, for a given partition strategy, the existence an optimal access-
backhaul split maximizing the rate coverage probability is shown. As a third
point, Saha et al. show that IAB-enabled network outperforms the macro-only
network up to a certain point, beyond which the performance gains disappear
and they converge to the same point.

Huang et al. propose a fairness-based distributed resource allocation
algorithm (FDRA) [62]. Huang et al. use stochastic geometry to model the two-
tier HetNet and define the channel reuse radius based on the spectrum sensing
threshold. FDRA is proposed to maximize the total throughput in the small cell
tier while considering the outage probability and fairness. To model the real
traffic load accurately, tidal effect and users’ actual geographical
distribution are jointly considered and improved FDRA is proposed to further
improve the throughput in hot areas. Simulation results show that FDRA
improves fairness compared to the benchmark method CSRA. In all metrics, IFDRA
outperforms FDRA while FDRA in turn outperforms CSRA.

In [63], Pham et al. consider the effects of resource offloading for mobile
edge computing on the small cells that use wireless backhauling to a MBS. The
authors highlight that the resource offloading research mostly assumes there
is backhaul capacity for their purpose, but the real-life situation does not
conform to that assumption. The authors decompose this joint task offloading
and resource allocation into two parts. In the first part, the decision to
offload the computation based on a given bandwidth factor and computation
resource allocation is made; whereas in the second part, wireless backhaul
bandwidth and computation resource allocation is made for a fixed offloading
decision. These two sub-problems are solved individually with an iterative
algorithm for the solution of the original problem. Moreover, the authors have
extend the problem into multiple scenarios, such as ultra-dense networks,
machine learning usage for computation offloading, and partial offloading of
the computation. The proposed algorithm is compared to the given problem’s two
extremes where all computations are either performed locally or offloaded to
the mobile edge computing server. As expected, with an increasing number of
UEs, fewer offloading decisions are made, since the available resources
decrease with a larger number of UEs. Furthermore, the proposed algorithm
achieves lower computational overhead since its decisions are made only when
offloading is advantageous. The same performance improvements are also seen in
UEs’ transmit power because the algorithm considers the consumed power while
offloading for its decision-making.

Song, Liu and Sun consider the joint radio and computational resource
allocation problem in NOMA-based MEC in HetNets [64]. Song et al. minimize the
energy consumption while guaranteeing task execution latency. The optimization
problem is solved using decomposition and iteratively employing alternating
optimization. Simulation results show that partial offloading outperforms full
offloading and local computing in terms of energy efficiency. Increasing task
tolerance latency increases energy efficiency for all configurations.

In [65], Han et al. investigate the user association and resource allocation
problem in the mMIMO-enabled HetNets. The network in question uses renewable
energy but is also connected to the grid in case of deficiencies. Han et al.
formulate a problem of network utility maximization subject to backhaul,
energy, and resource constraints. The given problem is solved using first a
primal decomposition, and then a Lagrange dual decomposition. In the first
decomposition, the problem is divided into two parts: the lower-level problem
i.e. resource allocation problem of BSs and higher level problem i.e. cell-
association problem of users. The lower level problem is also divided into $N$
subproblems for individual BSs. The higher level problem is solved using the
solution of resource allocation problem. To make this a distributive
algorithm, the Lagrange dual decomposition is used to divide the problem into
dual and master problems. In the resulting distributive algorithm, the UEs
calculate and choose the optimal BS allocation at their side and report this
connection to BSs; whereas BSs solve the dual problem and then broadcast the
next turn’s information to users. Furthermore, Han et al. design a virtual
user association and resource allocation (vUARA) scheme, which reduces the
communication overhead over the air interface and removes the information
leaks to other users. In this scheme, after the initial resource allocations
are made and user associations are reported, the measurements are reported to
the radio access network controller (RANC), in which vUARA resides. RANC
simulates the iterative cycles and after finding an optimal user association
and resource allocation, reports this to the users and BSs. The proposed
algorithm outperforms the common user-association schemes, i.e., max-SINR
association and range expansion association.

Le et al. consider network and weighted sum energy efficiency of dense HetNets
[66]. The authors propose a resource allocation algorithm supporting both
delay-sensitive users with minimum data rate requirements and delay-tolerant
users with data rate fairness considerations. The problem is formulated and
solved in three different ways. Mixed-integer programming, time-sharing, and
sparsity-inducing formulations are employed to tackle the nonconvex functions
with efficient sub-optimal solutions. All formulations make use of successive
convex approximations to find the solutions iteratively. Numerical results
show that the proposed formulations perform quite similar from an energy
efficiency perspective in terms of static consumed power and maximum transmit
power at femtocells. Mixed-integer programming formulation is shown to be
superior in terms of energy efficiency to other approaches when users’ QoS and
number of femtocells are considered.

In [67], Omidvar et al. propose a hierarchical optimization method for HetNets
with flexible backhaul. In flexible backhaul, only some network elements are
connected to the core with fiber connection and the rest of the network must
reach to these BSs via wireless connections with dynamic links. The authors
formulate a two-timescale hierarchical RRM control problem for HetNets with
flexible backhaul. For the overall problem, RRM control variables are divided
in two as long-term and short-term control variables. For long-term control,
flow control, routing control and discontinuous transmission control are used
whereas for short-term control, link scheduling control is used. From these
variables the problem is formulated. The problem is then divided into two sub-
problems by using primal decomposition. The inner problem becomes the
optimization of routing control and flow control under a fixed DTX control and
link scheduling policy. For the outer problem, an iterative algorithm is
proposed whose solution is shown to be optimal. At each subframe, the
scheduling information is sent to the UE and UE sends back the instantaneous
SINR value as feedback. At each superframe, the RRM system receives the
average associated link rates of BSs and sends back the next DTX time-sharing
scheme, routing and flow control updates. The proposed algorithm performs
better than five chosen baseline methods in terms of network utility for
different backhaul connected BS percentage and BS power. The algorithm
converges faster than all baseline methods, has similar signaling overhead, is
shown to be more cost-saving compared to the baseline methods and adds only
little overhead in terms of CPU utilization.

Gao et al. propose a quantum coral reefs optimization algorithm for joint
resource allocation and power control problem in D2D HetNets [68]. Gao et al.
first propose a model for cooperative D2D HetNets and then derive analytical
formulas for the total throughput of this model. A combination of coral reefs
optimization algorithm and quantum evolution are proposed as a novel quantum
coral reefs optimization algorithm (QCROA) to optimize the resource allocation
and power control problem. The strength of this approach is that the same
algorithm can be used for different parameters by changing the parameter
vector, which showcases the reusability of this algorithm. Simulation results
show that the proposed algorithm outperforms all alternative methods in
throughput under different maximum power, cellular unit and idle relay count
constraints.

In [69], Liu et al. have considered the resource allocation problem on a two-
tier network that uses mMIMO on the MBS and in-band full-duplex communications
on small cells. User association and spectrum allocation in the aforementioned
network scheme is formulated as a mixed-integer nonlinear programming (MINLP)
problem. To solve this problem distributively and efficiently, the primary
problem is divided into two sub-problems, namely the user association problem
for fixed spectrum allocation, and spectrum allocation with given user
association. Next, an iterative algorithm is developed to solve the original
problem. The proposed algorithm is compared with the max SINR user association
algorithm and shown to be outperforming it in terms of sum logarithmic rate.
It is also shown that the optimal spectrum allocation outperforms any fixed
spectrum allocation scheme.

Song, Ni and Sun investigate the distributed power allocation problem in non-
orthogonal multiple access HetNets [70]. The power allocation problem is
modeled as a two-stage Stackelberg game where MBS is the leader and SBS is the
follower. MBS tries to maximize its throughput under power and minimum rate
constraints whereas SBS tries to find the best response to MBS’ actions.
Simulations show that the proposed algorithm converges fast and is better in
terms of throughput compared to Nash game, dedicated spectrum allocation, and
orthogonal multiple access.

In [6], Bojic et al. have proposed a dynamic resource allocation algorithm for
mobile backhauled networks. The authors state that the problem with mobile
backhaul resource allocation is often the assigned capacity. If this is made
statically, resources are wasted in lightly loaded areas, whereas areas with
heavy loads will experience congestion. For dynamic assignment, the size of
the resource chunks assigned needs to be carefully considered; if the size is
too large, resources will be wasted, if they are small, then too many
signaling may occur. The authors propose a backhaul resource manager (BRM)
that fairly allocates the resources based on the network topology, link
capacities and resource allocations. BRM is linked with the network management
system and/or with the backhaul nodes to receive the needed information. From
the aforementioned information, the BRM will perform the capacity-aware path
computation. It is shown that the dynamic algorithm outperforms SPF by 40% and
CSPF by 20-30%.

Hu et al. propose a power allocation model to maximize energy efficiency in
HetNets [71]. The problem is modeled as a probabilistic fractional programming
optimization problem. Bernstein approximation and Dinkelbach method are used
to transform the problem into a standard convex approximation problem.
Simulation results show that the transmit power convergence is quite fast and
outperforms the rate maximization-based robust power allocation algorithm in
terms of energy efficiency. The proposed algorithm is slightly worse than the
energy-efficiency based optimal power allocation algorithm, but the
aforementioned algorithm considers perfect CSI, which is not available in the
actual communication scenario.

In [72], Faruk et al. have thoroughly analyzed the energy consumption
characteristics of macro and small cells. It is shown that wireless backhaul,
especially with PtMP microwave links, results in high power consumption
compared to other alternatives. BS equipment and cooling are also shown to be
the most power-consuming tasks. Self-backhauling is shown to be an alternative
to other wired and wireless backhaul methods due to its advantageous
characteristics such as reduced deployment and maintenance costs, fast rollout
and relaxed constraints compared to LoS deployments. The power consumption of
small cells with fixed wireless access to MBS and self-backhauled small cells
are compared. Since PtP links consume power independent from the load, they
are shown to be more power-efficient than self-backhauling as the BS load
increases. The break-even point is shown to be 50% load. It is given that
after 55W power consumption on fixed wireless radio links, they become less-
efficient than self-backhauling solutions. This result is more evident if the
number of MBSs increase. Lastly, it is shown that the implementation of PtMP
links where a single PtMP unit serves several small cells results in a more
power-efficient solution as the number of served small cells increase.

In [7], Wang, Hossain and Bhargava have delved into the topic of radio
resource management of 5G small cell backhauling. General aspects such as cell
association, RAT selection, resource and interference management, cell
coordination, spectrum sharing and energy-efficient backhauling are explored.
Then, some existing works about these aspects are briefly analyzed. Finally, a
case study is conducted with a mMIMO-based in-band wireless backhauling
network is given. It is shown that user rates can significantly be increased
with backhaul-aware cell association and bandwidth allocation schemes.

Siddique, Tabassum and Hossain compare the performance of in-band full-duplex
(IBFD) and out-of-band full-duplex (OBFD) backhauling [73]. Siddique et al.
formulate a problem to maximize the minimum achievable rate at the small cells
in a hybrid IBFD/OBFD setting. This problem is solved in a centralized fashion
by transforming the original problem into the epigraph form. The authors also
solve the individual IBFD and OBFD backhauling problems, which provides useful
insights into the nature of these schemes. Then, solutions are found for two
distributed backhaul spectrum allocation schemes, namely maximum received
signal power (max-RSP) and minimum received signal power (min-RSP). Max-RSP
scheme assigns a larger amount of backhaul spectrum to closer SBSs, offering
higher backhaul rates to SBSs at the cost of lowering the available access
spectrum. Min-RSP scheme conversely allocates larger backhaul spectrum to the
SBSs that are farther away from the MBS. Numerical results are validated using
Monte Carlo simulations. These simulations show some interesting results.
Hybrid allocation scheme favors IBFD for low interference and OBFD for high
interference settings. For OBFD scheme, access spectrum allocation seems to be
following the same pattern for lower rank (i.e. closer) SBSs as well as the
higher rank (i.e. farther) SBSs, but the backhaul spectrum required is higher
for higher rank SBSs due to weak backhaul signal strength. Conversely, IBFD
case lower rank SBSs are limited by backhaul interference. This starts to
improve with higher rank SBSs, which require higher access rates i.e. more
access spectrum for higher performance. It is shown that hybrid scheme
outperforms both individual schemes by prioritizing IBFD for low interference
and OBFD for high interference scenarios.

In [74], Shariat et al. have decomposed the overall resource allocation
problem between backhaul and access links to power and sub-channel allocation.
The optimization is first done between one small cell and a UE, and then were
generalized into multiple UEs. A novel algorithm is proposed for rate
balancing that employed small cell grouping and resource slicing. The
evaluation shows that joint optimization with rate balancing provides
significant improvements for resource allocation between access and backhaul
links. This method works not only for IAB systems, but it also shows
significant improvements for fixed backhaul partitioning systems.

Khodmi, Benrejeb and Choukair develop a joint power allocation and relay
selection algorithm for heterogeneous ultra-dense networks [75]. Khodmi et al.
use the increment water-filling algorithm for power allocation and a non-
linear programming algorithm for resource allocation. Simulations show that
multi-hop relay improves the coverage and achieves the data rate requirements
of cell-edge users compared to no relay or single-hop relay architectures.

In [76], Hao et al. have developed an energy-efficient resource allocation
algorithm for two-tiered networks that use mMIMO on the MBS and OFDMA-based
cellular frequencies on the small cells. A hybrid analog-digital precoding
scheme at MBS is proposed. From this scheme, a problem for power optimization
and subchannel allocation is formulated to maximize the energy efficiency of
the HetNet subject to users’ QoS and limited wireless backhaul. The formulated
problem is a MINLFP problem, and the authors made use of transformations,
namely MINLFP to DCP, and then DCP into convex optimization by appropriate
approximation, to solve it iteratively. In simulations, the proposed algorithm
is used with two structures: fully connected structure and subarray structure,
for beamforming RF chains. If subchannel allocation is not considered, then
the hybrid precoding scheme with subarray structure performs the best in
energy efficiency, while having the lowest throughput of three. The energy
efficiency of hybrid precoding with fully connected structure is negatively
correlated with the number of RF chains, whereas its throughput is positively
correlated. The results are similar when subchannel allocation is considered.

Mahmood et al. propose the adaptive capacity and frequency optimization method
for wireless backhaul networks [77]. Conventional network planning and its
shortcomings are mentioned in this work. Mahmood et al. generate synthetic
time series data based on real network data. This time series data is then
used to train autoregressive integrated moving average (ARIMA) and multi-layer
perceptron (MLP) models. These models are then used in the automated planning
algorithm. Results show that MLP models especially have very low mean absolute
percentage error (MAPE) and all models have performed below the error
threshold of 10. The proposed methods are shown to achieve better capacity
planning and optimization as well as reduce resource wastage.

In [78], Han and Ansari have developed a system for user association and power
consumption on two-tiered HetNets with green energy and on-grid power usage.
The proposed algorithm works in a distributed fashion and optimizes the use of
green energy and the traffic delivery latency of the network. The system tries
to make use of green energy wherever possible over on-grid power. Moreover,
when forced to use on-grid power, the system balances the use of on-grid power
and the traffic delivery latency. The traffic arrival process is modeled to be
a Poisson process; therefore, base stations realize a M/G/1-PS (processor
sharing) queue. The algorithm, which the authors have named GALA, is
distributed to BS and users. The BS-side algorithm measures the traffic load
and updates the advertising traffic load. In contrast, the user-side algorithm
selects the optimal BS based on the advertised traffic load, BS’s energy-
latency coefficient and BS’s green traffic capacity. The developed algorithm
is compared with maximum rate algorithm and $\alpha$-distributed algorithm,
which consists of several optimization policies for user-BS associations to
balance the flow level traffic among BSs. The GALA algorithm is shown to
outperform both of the algorithms significantly in terms of power saving at
the cost of negligible latency increases compared to the $\alpha$-distributed
algorithm.

Ma et al. investigate the user association and resource allocation problem of
MIMO-enabled HetNets [79]. The network in question is a two-tiered HetNet
consisting of a single mMIMO MBS and multiple small cells. Ma et al. formulate
the problem of maximizing the $\alpha$-fairness network utility via user
association and resource allocation. The optimization problem is formed as a
mixed-integer nonlinear programming problem. Then, this problem is solved
using an algorithm based on the Lagrangian dual decomposition method.
Simulations show that the proposed method significantly outperforms the
benchmark methods such as optimal UA and equal RA (where all UEs associated
with a BS use the same resources) or max-SINR UA and optimal RA (UA is done
solely by checking max-SINR), while falling short of the global optimum only
by a very small margin.

Huskov et al. propose a smart backhaul architecture that adapts itself
according to given task [80]. The system can adapt itself to certain equipment
conditions for different equipment types defined previously. This development
aims to save power by reducing the amount of unused resources, while keeping
network at a high performance regardless of the load. The proposed system also
makes use of a concept called multiple-input distributed-output (MIDO). MIDO
aims to serve multiple devices on the same spectrum band providing
interference alignment. Simulations show that the usage of MIDO with the
proposed system outperforms standard MIMO systems 2.5 and 30 times for 2x2 and
8x8 configurations, respectively. However, Huskov et al. also highlight the
fact that employing higher number of antennas at receiver side is unrealistic.

Zhang et al. design a two step resource allocation scheme for mmWave enabled
IAB networks [81]. User association and transmit power allocation problem is
iteratively solved. User association problem is solved using a distributed
framework based on the many-to-many matching game. For this purpose, Zhang et
al. design a novel backhaul capacity and interference aware matching utility
function considering the interference penalty and backhaul capability
simultaneously. For power allocation, convex approximation is used. This
problem is then solved iteratively to converge to a KKT point. Simulations
show that the proposed algorithm outperforms the random matching algorithm by
up to 71.9% when the number of SBSs is 10.

Pu et al. formulate a resource allocation problem for mmWave self-backhauling
networks [82]. The problem is modeled as a combinatorial integer programming
problem. By introducing penalty factors, Pu et al. transform the problem into
another equal problem which can then be solved by the Markov approximation
method. The proposed algorithm maximizes the sum data rate and satisfies the
minimum traffic demands of all users while satisfying the backhaul constraints
of the individual SBSs. Simulation results show that the achieved data rates
in each UE is slightly bigger than the required data rate, and repeating BH
traffic is minimized with the proposed algorithm. In terms of spectral
efficiency, the proposed algorithm outperforms the benchmark methods of OBFD
and access-backhaul split significantly.

Dai et al. tackle sum-rate maximization problem via joint user association and
power allocation in H-CRANs [83]. Dai et al. solve the user association
problem first in a central fashion. The power allocation problem is formulated
as a generalized Nash equilibrium problem and then solved distributively using
variational inequality theory. Simulations show that the proposed algorithm
has considerably lower computational complexity and signaling overhead than
the centralized max sum-rate method.

Xu et al. give a detailed survey of resource allocation problem of 5G HetNets
[84]. Xu et al. first define different types of HetNets, and the resource
allocation problems related with these types. For traditional HetNets, Xu et
al. highlight transmit power, user association, and bandwidth allocation
optimization as general problems. For OFDMA-based HetNets, subchannel
allocation is another problem due to multiple orthogonal subcarrier usage. For
NOMA-based HetNets, cross-tier and co-channel interference are problems to be
considered alongside user fairness. For relay-based HetNets, relay selection
and transmit power allocation are recurring problems. For H-CRAN’s, inter-cell
interference, signal overhead reduction and baseband unit management are the
problems to be tackled. For multi-antenna HetNets, interference cancellation
and beamforming design are architecture-specific problems. Integration of new
techniques and technologies such as IRS and ultra-dense networks require
careful consideration for resource allocation problems to attain the best
performance possible.

TABLE II: A review of the papers on resource allocation based on their common points Papers |  mmWave |  User Association |  CoMP |  Relay Selection |  Bandwidth / Spectrum / Channel / Resource Block Allocation |  Power Allocation |  Computation Resource Allocation |  Access/ Backhaul Resource Allocation
---|---|---|---|---|---|---|---|---
[82] |  |  $\times$ |  $\times$ |  $\times$ |  |  $\times$ |  $\times$ |  $\times$
[61] |  |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$ |
[6] |  |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$ |
[81] |  |  |  $\times$ |  $\times$ |  $\times$ |  |  $\times$ |  $\times$
[83] |  $\times$ |  |  $\times$ |  $\times$ |  $\times$ |  |  $\times$ |  $\times$
[78] |  $\times$ |  |  $\times$ |  $\times$ |  $\times$ |  |  $\times$ |  $\times$
[79] |  $\times$ |  |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$
[69] |  $\times$ |  |  $\times$ |  $\times$ |  |  $\times$ |  $\times$ |
[65] |  $\times$ |  |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$
[80] |  $\times$ |  $\times$ |  |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$
[58] |  $\times$ |  $\times$ |  |  $\times$ |  $\times$ |  |  $\times$ |  $\times$
[75] |  $\times$ |  $\times$ |  $\times$ |  |  $\times$ |  |  $\times$ |  $\times$
[68] |  $\times$ |  $\times$ |  $\times$ |  |  |  |  $\times$ |  $\times$
[60] |  $\times$ |  $\times$ |  $\times$ |  |  $\times$ |  |  $\times$ |  $\times$
[73] |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  |  $\times$ |  $\times$ |
[74] |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  |  |  $\times$ |  $\times$
[7] |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  |  $\times$ |  $\times$ |  $\times$
[77] |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  |  $\times$ |  $\times$ |  $\times$
[62] |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  |  |  $\times$ |  $\times$
[66] |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  |  |  $\times$ |  $\times$
[76] |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  |  |  $\times$ |  $\times$
[70] |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  |  $\times$ |  $\times$
[71] |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  |  $\times$ |  $\times$
[72] |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  |  $\times$ |  $\times$
[59] |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  |  $\times$ |
[60] |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  |  $\times$ |  $\times$
[63] |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  |
[64] |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  |  $\times$
[67] |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$ |
[57] |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$ |

####  V-A2 Lessons Learned

While resource allocation is by no means a problem unique to wireless
backhauling, effective distribution of the resources is crucial for a
performing network. With this in mind, we highlight the following points:

  * •

Access/backhaul resource split is very important in terms of effective
resource usage, because employing a dynamic split makes the network adaptable
to different scenarios. Employing dynamic and fast algorithms that can adapt
to changing conditions is very useful for ultra-dense networks with wireless
backhauling.

  * •

Ultra-dense networking employs many small cells that a UE can connect to,
meaning that at any time instant, multiple BSs are available for UEs. While
this increases the network adaptability and redundancy, user association is
important for network performance as the small cells usually do not have good
range and require LoS, finding a suitable BS is important. From the network’s
perspective, the network can employ suboptimal user associations to keep up
the average performance at a certain level or achieve a certain minimum.

  * •

Transmit power allocation is important for the wirelessly backhauled networks
as high air interface usage may result in inter- and intra-tier interference.
Employing spatial multiplexing with the right transmit power allocation
effectively nullifies the interference from the UE side, while BSs employ
beamforming in tandem with power allocation to reduce the effect of
interference.

  * •

With the usage of MEC in 5G networks, computation offloading is a possibility
that can result in power savings and effective computation. However, while
wireless backhaul can allow offloading computation-heavy tasks, there may be
cases in which offloading can induce performance penalties to the air
interface, resulting in power wastage from the network side. Consideration of
air interface is required for computation offloading decisions in wirelessly
backhauled networks.

####  V-A3 Open Issues and Challenges

Resource management remains an open challenge for future networks. 3D
networking is a new concept that significantly changes the network design.
Because of this, resource allocation schemes also require a new approach for
3D networking for the best performance. Spectrum and interference management
also remains an important open issue for future networks [85].

With a myriad of new concepts being introduced in 5G networks and beyond,
networks are inevitably getting more and more complex. While new concepts come
with performance gains, they also introduce complexity to the design of a
network. In terms of resource allocation, using multiple technologies
increases the complexity of the problem. New models are required in this
direction to allow better design; which, in turn, will allow better problem
formulations that capture the requirements and result in better performance
gains [84].

Spectrum sensing is another way to increase resource efficiency. The tradeoff
between dynamic resource allocation and spectrum sensing capabilities remain
an open research area that can result in better spectrum and resource
efficiency.

The majority of the mentioned works do not use mmWave frequencies.
Consequently, most mentioned works cannot meet the KPI demands of 5G
communications in terms of throughput. While beamforming and mMIMO are
technologies that can enable better performance with mmWave usage, resource
allocation problems have to consider beamforming and mMIMO usage alongside the
main problem, which adds more degrees of freedom and makes the problem even
harder. Due to these points, mmWave frequencies and the resource allocation
schemes require a more in-depth exploration.

Machine learning-based resource allocation is also promising since it allows
autonomous management of networks. BSs can capture an enormous amount of data
that can be used for training a model for user association, transmit power
calculation, or spectrum allocation. While these are open research areas, the
training process is a problem since there aren’t enough resources for training
in one BS. Therefore, either distributed algorithms are required for this
task, or a new paradigm must be developed in order to realize machine-learning
based dynamic resource allocation.

###  V-B Deployment

Base station deployment is a problem not specific for wireless backhauling.
Nonetheless, deployment plays a critical role when wireless backhaul is
employed. Here, the challenge is where to place MBSs and small cells. Wireless
backhaul enables more radical deployment strategies since infrastructure (or
the lack of it) has less of an impact compared to traditional backhaul.
Similar to the traditional networks though, deployment directly impacts
network performance. Therefore, innovative deployment schemes have to be
employed to make wireless backhauling feasible.

Mesh, star and tree topologies are usually considered while deploying
networks. Tree topologies are especially used with hierarchical networks, as
well as with wireless backhauled networks. In such schemes, root node of the
tree has fiber backhaul capabilities, and all other nodes send their data via
their parent to the root node to be backhauled to network core. Mesh topology
is also suitable to be used with wireless backhauling as it promotes
redundancy via multiple paths. Finally, wireless backhauling also allows
dynamic deployment with mobile base stations, which improves network’s
capabilities while making management and deployment harder.

While the deployment of small cells is a task performed before the actual
deployment, moving elements such as UAVs and nomadic BS make this problem
dynamic. Because of this, finding the optimal deployment in a fast manner
becomes important as this increases the network adaptability and reduces the
time that the network is affected by the condition changes in a bad way.

####  V-B1 State of the Art

In [57], Bonfante et al. propose a two-layer network scheme where self-
backhauling small cells serve UEs and MBSs using mMIMO backhaul their data.
Small cells use fixed allocation of access and backhaul slots. Two different
deployment scenarios are considered for small cells. In the first scenario,
the small cells are randomly and uniformly distributed to the BS area; whereas
in the second one, which the authors named as ad-hoc deployment, they are
deliberately placed closer to UEs to increase the throughput. To reduce the
inter-cell interference, small cells in ad-hoc deployment use a more directive
antenna that serve only the closest UEs, thus reducing interference. Results
show that the performance increases threefold when ad-hoc deployment is used
compared to uniform deployment.

McMenamy et al. investigate mmWave backhaul network flow affected by the
number of hops and deployment of fiber-connected BSs [86]. The authors first
determine which nodes will have fiber backhauling given the deployment while
satisfying the constraint on the number of hops. Then, the authors maximize
the overall flow while meeting the flow demand of every node and the
constraint on the number of RF chains for every node in the network.
Simulation results show that increasing the number of hops reduces the number
of required fiber-connected BSs (BGWs), but also the total flow. Number of RF
chains grows with the number of hops. If the number of BGWs are reduced, then
the performance can be maintained by increasing the number of RF chains at the
expense of power consumption.

Aftab et al. address UAV-BS placement problem with a novel machine learning
based intelligent deployment mechanism [87]. An intelligent long short-term
memory (LSTM) model is used to predict the deployment with a feedback
mechanism for self-correcting. Simulation results show that deep learning-
based approach outperforms the normal deployment in terms of outage
probability. ML-based deployment also achieves far better energy efficiency
compared to the normal deployment.

In [88], Fouda et al. have considered a scenario where UAV are deployed to a
highly congested area where some UEs suffer low quality and some have no
service at all. The network in question is a two-tiered network with gNB’s
that serve UEs in tandem with UAV small cells. The network uses in-band
frequency division duplexing (FDD) as the operation mode. The UAV s’ mobile
capabilities are used as the main degree of freedom to maximize the overall
system sum-rate. The authors have formulated an optimization problem to find
the UAV s’ optimal 3D deployment locations, UE power allocations, precoder
design at gNB and UE-base station associations. Since these optimization
parameters have different update intervals, the problem is decomposed into two
parts: in the first part, average sum-rate is maximized by optimizing the UAV
locations, access and backhaul link power allocations; whereas in the second
part, precoder design and transmit power allocation is optimized to maximize
the instantaneous sum-rate. Simulations show that UAV deployment increases the
average sum-rate at the expense of performance of UEs that are close to gNBs.
It is also shown that UAV s cannot support a high number of UEs as efficiently
and distributing users between UAV s is key to a high-performance network.

Park, Tun and Hong propose initial deployment and trajectory optimization
techniques for stable communication between high speed train (HST) and UAVs
[89]. Park et al. use Soft Actor-Critic (SAC) method of reinforcement learning
for optimizing the UAV trajectory and Support Vector Machine (SVM) for optimal
initial UAV deployment. Numerical results show that with less HST speed, less
handovers occur and UAVs can have lower operating altitudes with higher data
rates. Conversely, with faster HST speeds, UAVs have to increase their
altitudes to keep the connection up.

Zhang et al. formulate a joint optimization problem of UAV deployment, caching
placement and user association for maximizing the QoE of users [90]. The
cache-enabling UAVs are deployed at peak hours to the hotspots to alleviate
the pressure on the MBSs. The formulated optimization problem is divided into
three subproblems. UAV deployment subproblem is solved via a swap matching
based algorithm. Then, Zhang et al. solve the caching placement subproblem
with a greedy algorithm, and user association problem with a Lagrange dual.
The joint problem is solved with a novel low complexity iterative algorithm.
Simulation results show that the proposed algorithm can reach the near optimal
value of the exhaustive search within 0.02 mean opinion score (MOS). The
proposed algorithm outperforms the random and classic algorithms in terms of
UAV backhaul traffic offloading ratio, and average MOS, under varying user
numbers, cache space, and UAV height.

Okumura and Hirata propose an algorithm to automatically plan the deployment
of 300 GHz wireless backhaul links [91]. The algorithm first selects a pair of
adjacent cells that can get a LoS connection. After deploying a master BS to
the center rooftop, the algorithm deploys the slave BSs according the the
previously mentioned pairs of adjacent cells to find a LoS path from every
slave BS to the master BS. Simulation results show that this deployment scheme
exceeds the standart demand of 100 Gbit/s even when the rain rate is 100
mm/hr.

In [9], Polese et al. have explained the integrated access and backhaul
concept and explored some scenarios for its real-life usage. A thorough
explanation of IAB is made, its architecture, network procedures, topology
management, scheduling and resource multiplexing aspects are detailed. Then,
an evaluation of an IAB mmWave network is made through simulations with
different configurations. For routing the backhaul traffic, wired-first and
highest-quality-first approaches are tested and wired-first approach is found
to be better in terms of throughput and latency. For IAB deployment scenarios,
three alternatives are compared: namely, only donors scenario in which only
some wired gNBs exist, IAB scenario where alongside the first scenario donors,
some IAB nodes are available, and all wired scenario where it is assumed that
the topology is the same with the second scenario but all backhaul connections
are wired. All wired scenario outperforms the others as expected, but IAB
scenario outperforms the donors-only scenario in average delays, 5th
percentile throughput and average buffering in target UEs.

Hu et al. propose an intelligent UAV-BS deployment scheme based on machine
learning [92]. Hybrid ARIMA-XGBoost model is used for predictions: ARIMA
handles linear predictions and XGBoost is then applied on the residue of
ARIMA. Simulation results show that the proposed hybrid prediction model has a
prediction accuracy of 87% in the worst case. The proposed deployment scheme
also significantly reduces the blocking ratio, increases the capacity,
throughput, and resource utilization of the network.

In [49], Dahrouj et al. have proposed a hybrid system that uses RF as well as
FSO links for wireless backhauling. The hybrid usage combines the low-cost,
high-availability and high-coverage nature of RF and high-capacity, low-
latency nature of FSO, bringing the best of both worlds. To reduce complexity,
the problem is first relaxed such that only optical fiber links are allowed.
Using the found planning, the found nodes are connected with fiber or hybrid
RF/FSO links. To improve reliability, the planning is made so that the network
is $K$-link disjoint. This is done by clustering the BSs together and
iteratively finding the links between clusters. The authors show that the
solution is equivalent to a maximum weight clique in the planning graph. The
results indicate that as $K$ increases, the network design is cheaper than
fiber-only while maintaining the required reliability and key performance
indicators (KPI s).

TABLE III: A review of the papers on deployment issues based on their common points. Papers |  mmWave |  MIMO |  Machine Learning |  Multi-Hop |  UAV Deployment |  FSO
---|---|---|---|---|---|---
[93] |  |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$
[9] |  |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$
[86] |  |  $\times$ |  $\times$ |  |  $\times$ |  $\times$
[91] |  |  $\times$ |  $\times$ |  |  $\times$ |  $\times$
[94] |  |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$
[57] |  |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  $\times$
[87] |  $\times$ |  $\times$ |  |  $\times$ |  |  $\times$
[89] |  $\times$ |  $\times$ |  |  $\times$ |  |  $\times$
[92] |  $\times$ |  $\times$ |  |  $\times$ |  |  $\times$
[95] |  $\times$ |  $\times$ |  |  $\times$ |  |  $\times$
[96] |  $\times$ |  $\times$ |  $\times$ |  |  $\times$ |  $\times$
[97] |  $\times$ |  $\times$ |  $\times$ |  |  $\times$ |  $\times$
[49] |  $\times$ |  $\times$ |  $\times$ |  |  $\times$ |
[88] |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  |  $\times$
[98] |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  |
[90] |  $\times$ |  $\times$ |  $\times$ |  $\times$ |  |  $\times$

Liu et al. investigate a fast deployment strategy of UAVs using deep learning
[95]. Liu et al. formulate a problem of finding the optimal UAV-BS position as
fast as possible, while maximizing the sum of downlink rates in the network.
The authors solve this problem by designing a geographical position
information learning algorithm, which employs a dueling deep Q-network.
Simulation results show that the proposed algorithm finds the deployment
position in seconds with little loss. While reinforcement learning requires
training after any changes in the scenario, the proposed algorithm does not
have such a requirement and achieves 96% of the optimal value. For different
area sizes and UE densities, the proposed algorithm performs almost
indistinguishably from reinforcement learning but with less time.

Zhang et al. optimize the BS and gateway densities of an mmWave backhaul
network to improve network spatial throughput [94]. Low density makes the
network backhaul-limited, but after a certain density, the performance gains
disappear because the network becomes interference-limited due to the intra-
tier interference of the newly added elements. The optimal densities are found
numerically and the simulation results show that with the increasing network
density, the spatial throughput first increases and then converges to a
gateway density-related constant.

Kulkarni, Ghosh and Andrews [97] propose a network with k-ring deployment with
wireless backhauling. In k-ring deployment, a BS with fiber connection is on
the centre while all other BSs are backhauled wirelessly to this BS, and then
to the core. Kulkarni et al. argue that with their design, instantaneous rate
performance is independent of the scheduling, which makes the system noise-
limited. The authors formulate an optimization problem for a fixed k that
maximizes the end-to-end rate achieved by all UEs. For this purpose, Kulkarni
et al. define the max-min rate as the maximum rate that can be achieved by the
worst member of a routing strategy. The analysis is done initally for IAB, and
then extended to orthogonal access and backhaul (OAB), which basically means
different resources for access and backhaul. For IAB case, Kulkarni et al.
employ highway routing, which designates the $x$ and $y$ axes that are
centered on the fiber backhauled BS as the highways. The data is then
distributed from these highways, even if this results in more hops than a
direct path. If BSs on the highways have larger antenna gains than others,
this scheme is justified. Nearest neighbor routing is used as the benchmark
scheme. IAB solution requires the global load and access rate information,
which makes its solution more complex. OAB is simpler to implement since it
does not require such information. Kulkarni et al. present results in four
main fields. First, it is shown that with higher number of rings, the
performance drops significantly since the network is backhaul-limited. Second
comparison is made between full-duplex and half-duplex modes. Full-duplexing
shows significant potential in terms of data rate if the self-interference is
kept below -100 dB. However, Kulkarni et al. also note that these results do
not seem practical. Third comparison is made between OAB and IAB. It is shown
that OAB can slightly outperform IAB if the access/backhaul resource
assignment is made according to the load. If this assignment is made in a
fixed fashion regardless of the expected load, then IAB is the better choice.
Finally, dual and single connectivity are compared in terms of performance. It
is shown that, performance gains with dual connectivity is higher if there are
load imbalances in single connectivity cases.

Zhang and Ansari investigate the backhaul-aware uplink communications in a
full-duplex drone BS-aided HetNet problem with the objective to maximize the
total throughput while minimizing the number of deployed BSs [98]. The authors
decompose the problem into three parts, namely (i) joint user association,
power, and bandwidth problem, (ii) drone BS placement problem, (iii)
determining the number of drone BSs to be deployed. The first problem is
solved using an approximation algorithm. The second problem is solved using
exhaustive search, while the third problem is solved using linear programming.
Simulation results show that the blocking ratio of the proposed algorithm
outperforms the benchmark methods in terms of total throughput and the
blocking ratio.

Saadat, Chen and Jiang consider multi-hop backhauling for ultra-dense networks
[96]. The authors propose a deployment scheme that allows multiple backhaul
paths to MBSs, helping overcome the blockage problem to some extent and
improve overall reliability. The proposed method for backhaul is called the
multipath multi-hop (MPMH) backhaul. Saadat et al. assume that the small cell
density is much higher than the user density, and each SBS only serves one
user. User-serving SBSs relay their backhaul traffic via the closest SBS to
the MBS. The backhaul routing is based on minimizing the number of hops. The
proposed MPMH backhaul is compared with direct backhauling, multihop
backhauling and multiple-association backhauling that employs multiple paths
between sender and receiver. Simulation results show that MPMH backhaul
outperforms all other alternatives regardless of the inter-site distance. It
is also shown that multi-hop scheme significantly improves LoS probability.
Finally, as the inter-site distance increases, multipath scheme shows
significant gains over multi-hop scheme. Needless to say, MPMH backhaul
incorporates the advantages of both schemes together.

Nasr and Fahmy analyze the scalability of ring and star topologies in mmWave
backhaul networks [93]. The network in question has one MBS with fiber
connectivity and multiple small cells with star or ring topology. Mesh
topology uses multiple hops to relay traffic to the MBS, whereas in star
topology, every small cell has direct connection to the MBS. Simulation
results show that initially, star topology performs better but as transmit SNR
increases, mesh topology performance increases faster. Similar results can be
observed for different small cell sizes as well. Increasing small cell numbers
almost linearly increases the performance for both topologies.

####  V-B2 Lessons Learned

We highlight the following points for the deployment problem:

  * •

UAVs are used for different purposes, such as content caching and performance
improvement. UAV deployment directly affects the performance gains from such
use cases. It is shown that with correct deployment, UAVs can be used as small
cells with better freedom and adaptability.

  * •

With the new ultra-dense network architectures, hierarchical designs seem to
be advantageous. Using tree structures allow better flow designs for
backhauling, while mesh structures allow better flexibility and redundancy by
employing multiple links for backhauling. Mesh structures employ every small
cell as both an access node and a backhaul node for other small cells, whereas
tree structures make use of small cells that perform backhauling most of the
time, especially for nodes that have higher heights in the tree structure.

  * •

Machine learning approaches are widely employed with UAV deployment problems,
and with solid results. Machine learning approaches are also shown to be quite
fast, which is a significant step to realize a self-organizing network with
UAVs. Compared to the optimization approaches using linear programming and
similar formulations, machine learning approaches give flexibility to the
network to adapt to different conditions rapidly.

####  V-B3 Open Issues and Challenges

Current research on UAVs do not make use of mmWave frequencies. In the future,
UAVs can use mmWave frequencies for better performance. The deployment of
mmWave-using UAVs is an open research problem that requires investigation.
This problem has different facets such as vertical modeling, beam management,
and antenna design.

While UAVs and other mobile cell options provide flexibility for 5G networks,
current approaches are still not fast enough to be used for self-organizing
networks on a grand scale. Current formulations are often optimization
problems that require considerable time for solving. Consequently, these
solutions do not use the mobility capabilities to the fullest extent. Faster
and more adaptable approaches are required for self-organizing networks and
this problem remains an open challenge. While some machine learning approaches
are performing in this area, there is still room for improvement, especially
for larger-scale self-organizing networks.

UAV and mobile cell mobility also presents problems for mesh and tree
topologies, as the moving cells alter the topology and adaptation to these
movements are required. This problem can be solved in a central or distributed
fashion. While each seem to have their own advantages, distributed approaches
seem to have an edge since the cells have a high volume of base stations. The
adaptability of topologies is an open challenge that needs an effective
solution for a moving network with high performance. Table III gives a summary
of the papers mentioned in this section.

###  V-C Scheduling

Similar to the deployment problem, scheduling is a general problem as well.
Here, we consider the scheduling of access and backhaul traffic, which is an
essential aspect of wireless systems when both access and backhaul uses the
same medium. Employing a good scheduling scheme for access and backhaul
traffic is imperative to attain performances up to the network’s potential.

####  V-C1 State of the Art

In [99], Vu et al. consider a HetNet with a MBS and its subordinate small
cells that use wireless backhauling. In such a system, Vu et al. point out
that the cross-tier (between MBS and small cells) and co-tier interference
(between small cells) becomes a significant problem that reduces the
throughput. Therefore, a joint optimization of scheduling, operation mode,
interference mitigation and transmit power allocation that satisfies the
available transmit power budget is considered. Small cells operate either
half-duplex to reduce interference, or full-duplex to increase performance.
The proposed system is compared with a traditional network in which MBS serves
the UEs without any small cells. It is shown that for three configurations
that are modeled at 28 GHz, 10 GHz and 2.4 GHz, the proposed network
outperforms the traditional network in both achievable average throughput and
average queue length.

Gupta et al. consider the multihop link scheduling problem in self-backhauled
mmWave cellular networks [100]. The authors formulate the given problem as a
Markov decision process and use reinforcement learning techniques to solve it.
The training is conducted for two scenarios, namely an ideal case where a
central scheduler performs all scheduling decisions with full knowledge at any
instant, and a more realistic case where network state feedback and scheduling
decisions are limited to one per frame. For the realistic case, Gupta et al.
model the scheduler as a recurrent neural network. Simulation results show
that the proposed method outperforms the other benchmark methods (max-min,
backpressure, DRQN, r-REINFORCE) in terms of packet delay for one-hop, two-
hop, and three-hop users at high- and medium-load scenarios.

Zhang et al. propose a joint cooperative orthogonal and non-orthogonal
multiple access wireless backhauling model [101]. Using this model, the
authors formulate an optimization problem to maximize energy efficiency via
resource block scheduling. Zhang et al. try to maximize the energy efficiency
while meeting the given rate threshold of SBSs. Orthogonal multiple access
(C-OMA) can make use of good channels in a more effective way, while non-
orthogonal multiple access (C-NOMA) consumes less power with the same
threshold. Using these two methods together is much more complicated than a
mixed integer non-linear programming problem. Zhang et al. propose a greedy
algorithm to use the two multiple access methods in tandem. Simulation results
show that the combined greedy algorithm (JCC) is the best in terms of average
power consumption and energy efficiency. C-OMA performs close to JCC for
smaller cell radii while C-NOMA does the same for larger radii.

Arribas et al. study the mmWave backhaul scheduling problem for mmWave MBS and
microBSs [102]. The problem is modeled as a mixed integer linear programming
problem and the problem is NP-hard even when not considering the interference.
Arribas et al. establish upper and lower bounds of the problem and formulate
the problem as a simpler linear program to approximate the solution. Two
heuristic methods are also provided for comparison. Experimental result show
that the optimal method for small sizes perform close to the lower bound,
while for larger sizes optimal cannot be solved in a reasonable time. Greedy
heuristics perform closer to the upper bound, while ‘resched‘ heuristic method
performs closer to the lower bound. Greedy heuristic loses its edge when
multiple RF chains can be employed. Resched heuristic performs better than
greedy in terms of aggregate rates.

Li et al. perform distributed scheduling in an industrial wireless network
using reinforcement learning [103]. Li et al. propose a learning-based
autonomous orchestration agent, namely L-AoI, for timely state updates in
D2D-enabled networks. The proposed orchestrator guides distributed users to
select a proper resource block and power level to deliver state updates. Since
D2D users do not have global CSI or knowledge of other users’ scheduling
actions, the orchestrator allows each users to forecast other future
decisions. Simulation results show that the proposed orchestrator outperforms
Q-learning and Q-coalition methods in terms of age of information violation,
average data rate, and average power consumption.

In [104], Goyal, Liu and Panwar propose a scheduling system that works on a
two-tiered network with MBS and small cells. Small cells and MBSs support
full-duplex operation and the scheduler decides in each time step which
transmission mode will be used. The transmission mode is chosen such that the
maximum gain capacity can be achieved. Moreover, queue stability is also
considered while deciding to schedule as not to introduce congestion. For the
overall solution, a well-known back-pressure based scheduling algorithm that
is throughput-optimal is used. For the full-duplex operation, power management
is also considered to minimize the interference. It is shown that the
scheduler performs well to reduce the interference, so much so that the
performance gains are almost double (95-85%) compared to half-duplex only
operation. However, after a certain point the bottleneck results from the
weaker backhaul links. The authors also propose using directional antennas for
backhaul between small cells and MBSs, strengthening the links considerably
and improving overall cell throughput.

Rasekh, Guo and Madhow formulate the wireless mesh backhaul design problem as
a joint routing and resource allocation optimization for mmWave picocells
[105]. The optimal resource allocation is modeled as scheduling the time into
slots in which only a subset of all available links are active. This decision
is made according to the interference levels since interference is directly
related to the available data rate. Simulation results show that the proposed
scheduling scheme outperforms the interference-agnostic scheduling by 25% in
terms of backhaul throughput. This figure becomes higher as link SNR
increases.

Niu et al. formulate the joint optimization problem of concurrent transmission
scheduling and power control into a MINLP problem [106]. A contention graph is
used to model the concurrent transmissions. Then, a maximum independent set
(MIS)-based algorithm is used to allocate flows into different pairings. This
algorithm controls the interference between links and schedules as many links
into each pairing as possible, exploiting concurrent links to its fullest
potential. This algorithm is shown to achieve better performances than TDMA
scheme. However, concurrent transmission scheme with maximum transmission
power (CTFP) outperforms this scheme at the cost of higher energy consumption.

Hu and Blough explore the relay selection and scheduling problem for mmWave
relay-assisted backhaul [107]. Hu and Blough propose a new mmWave backhaul
network architecture where base stations serve as mesh nodes in the backhaul
and are connected via multi-hop paths. Moreover, the authors also propose a
linear-time scheduling algorithm achieving max throughput when there is no
secondary interference and a relay-selection algorithm to select a path with
no secondary interference. An algorithm is proposed that finds the minimum
scheduling length by employing the maximum demand sum of two consecutive
links. This algorithm is proven using induction. For relay selection, the
authors employ depth-first search using the number of hops and considering
only LoS paths. Numerical results and simulations show that even for short
distances from the MBS (¡ 200m), relays are required as LoS paths are not
available. It is shown that throughput is 10 Gbps at the worst case
(800-1000m). While minimum number of hops deliver adequate performance, going
over this number improves even further, but after a certain number of extra
hops (3) the gains plateau. It is also shown that the proposed algorithm
results in a throughput gain of 23% to 49%.

Forouzan et al. consider the joint user scheduling, operation mode selection,
and power allocation in a two-tier cellular network with full duplex capable
small cells [108]. The authors design a distributed resource allocation scheme
determining user scheduling, operation mode of access/backhaul links, and the
transmission powers to maximize the overall network throughput. The user
scheduling and operation mode problems are solved locally for each cell. The
power allocation problem is solved using successive convex approximation
method. Numerical results show that the proposed algorithm outperforms IBFD-
or OBFD-only approaches in terms of average network sum rate. Increasing the
number of SBSs also increases the average network sum rate.

TABLE IV: A review of the papers on scheduling based on their common points. Papers |  mmWave Scheduling |  Machine Learning |  Full-duplexing via Scheduling |  Interference Management
---|---|---|---|---
[109] |  |  $\times$ |  $\times$ |  $\times$
[110] |  |  $\times$ |  |  $\times$
[107] |  |  $\times$ |  $\times$ |  $\times$
[105] |  |  $\times$ |  $\times$ |  $\times$
[99] |  |  $\times$ |  $\times$ |  $\times$
[100] |  |  |  $\times$ |  $\times$
[102] |  |  $\times$ |  $\times$ |
[103] |  $\times$ |  |  $\times$ |  $\times$
[108] |  $\times$ |  $\times$ |  |  $\times$
[104] |  $\times$ |  $\times$ |  |
[110] |  $\times$ |  $\times$ |  |  $\times$
[106] |  $\times$ |  $\times$ |  $\times$ |
[101] |  $\times$ |  $\times$ |  $\times$ |

Yuan et al. consider the scheduling problem for networks using mmWave small
cells [110]. Due to mmWave characteristics, it makes perfect sense to make use
of spatial reuse via scheduling multiple links. All small cells (named mmBSs)
have only one RF chain available, effectively making the communication half-
duplex. Yuan et al. formulate the problem of maximum throughput fair
scheduling for backhaul networks. This problem is solved by the ellipsoid
algorithm. This optimization is scheduling oriented, and one downside is that
it can require a high runtime if the network has a large number of mmBSs. Due
to this, the authors also propose an edge-coloring based approximation
algorithm. This method works by computing the link time first, and then
scheduling based on edge-coloring. The proposed method is shown to be
applicable to access and backhaul networks, as well as multiple RF chains at
each node. Numerical evaluations show that the proposed EC-based method
achieves 70% to 90% of the optimal throughput with 100x better scalability.
With multiple RF chains, EC-based method achieves 85% to 90% of the optimal
throughput while using 2% to 20% of the time.

Yuan et al. investigate scheduling algorithms under different interference
models [110]. The authors design a scheduling algorithm to optimize mmWave
backhaul efficiency for full-duplex and half-duplex radios assuming pairwise
link interference or not, and realistic or maximum single-user spatial
multiplexing . For the full-duplex no-interference model, Yuan et al. provide
a polynomial-time optimal algorithm for the maximum throughput fair scheduling
problem (MTFS). Adding pairwise link interference or half-duplex mode makes
the problem NP-hard, but the authors provide a special case of half-duplex
MTFS problem which is solvable in polynomial time. For NP-hard problems, the
authors provide approximation algorithms which transform the MTFS problem into
a fractional weighted vertex coloring problem. Numerical evaluation results
show that mean throughput increases as the number of RF chains increase. Full-
duplex approximation algorithms are shown to achieve 90% performance of the
optimal algorithms, while this figure decreases to 70-60% for half-duplex
approximation algorithms.

Sahoo, Yao and Wei present a multi-hop relay scheduling algorithm to maximize
the overall system performance for mmWave backhauling [109]. The network in
question has small cells that operate with PtP mmWave backhaul and fiber-
connected MBSs. A dynamically adaptable time division duplexing TDD scheme is
used for scheduling. A non-linear integer programming problem is formed to
optimally schedule the backhaul traffic according to bandwidth, power, and
half-duplex traffic. The authors propose a heuristic algorithm to solve this
NP-hard problem. In the first scheduling policy, local traffic load at each BS
is considered. In the second scheduling policy, links with higher channel
quality are prioritized. Individually, the first policy (Load-iBS) tries to
transmit maximum aggregate traffic, whereas the second policy (Link-iBS) tries
to maximize the overall network throughput via maximizing the traffic over
high-quality links. A third policy (TO-iBS) is proposed to use the
aforementioned concepts in tandem to achieve better performance. Load-iBS is
used to determine transmitting and receiving nodes, and then Link-iBS is used
for power reallocation to improve the active P2P link quality. Simulation
results show that TO-iBS outperform both Load-iBS and Link-iBS and performs
very similar to the exhaustive search scheduling. However, it is also much
easier to implement than the exhaustive search, making the advantages evident.

####  V-C2 Lessons Learned

Scheduling research on wireless backhauling is heavily invested on the mmWave
link scheduling and spatial reuse. We highlight the following learning points
for this area:

  * •