UAV Communications for 5G and Beyond E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Wiley - IEEE
• Sprache: Englisch

Seitenzahl: 824

Veröffentlichungsjahr: 2020

Beschreibung

Explore foundational and advanced issues in UAV cellular communications with this cutting-edge and timely new resource

UAV Communications for 5G and Beyond delivers a comprehensive overview of the potential applications, networking architectures, research findings, enabling technologies, experimental measurement results, and industry standardizations for UAV communications in cellular systems. The book covers both existing LTE infrastructure, as well as future 5G-and-beyond systems.

UAV Communications covers a range of topics that will be of interest to students and professionals alike. Issues of UAV detection and identification are discussed, as is the positioning of autonomous aerial vehicles. More fundamental subjects, like the necessary tradeoffs involved in UAV communication are examined in detail.

The distinguished editors offer readers an opportunity to improve their ability to plan and design for the near-future, explosive growth in the number of UAVs, as well as the correspondingly demanding systems that come with them. Readers will learn about a wide variety of timely and practical UAV topics, like:

• Performance measurement for aerial vehicles over cellular networks, particularly with respect to existing LTE performance
• Inter-cell interference coordination with drones
• Massive multiple-input and multiple-output (MIMO) for Cellular UAV communications, including beamforming, null-steering, and the performance of forward-link C&C channels
• 3GPP standardization for cellular-supported UAVs, including UAV traffic requirements, channel modeling, and interference challenges
• Trajectory optimization for UAV communications

Perfect for professional engineers and researchers working in the field of unmanned aerial vehicles, UAV Communications for 5G and Beyond also belongs on the bookshelves of students in masters and PhD programs studying the integration of UAVs into cellular communication systems.

Leseprobe

UAV Communications for 5G and Beyond

Edited by

Yong Zeng

Southeast University, China Jiangsu, ChinaandPurple Mountain LaboratoriesJiangsu, China

Ismail Guvenc

North Carolina State UniversityNC, USA

Rui Zhang

National University of SingaporeSingapore

Giovanni Geraci

Universitat Pompeu FabraBarcelona, Spain

David W. Matolak

University of South CarolinaSC, USA

This edition first published 2021

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The right of Yong Zeng, Ismail Guvenc, Rui Zhang, Giovanni Geraci, and David W. Matolak to be identified as the authors of this editorial work has been asserted in accordance with law.

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Library of Congress Cataloging‐in‐Publication Data

Names: Zeng, Yong (Professor at Southeast University ), author. |

  Guvenc, Ismail (Professor at North Carolina State University) | Zhang, Rui (Professor at National

  University of Singapore), author. | Geraci, Giovanni (Assistant Professor at Universitat Pompeu Fabra),

  author. | Matolak, David W., author. | John Wiley & Sons, Inc., publisher.

Title: UAV communications for 5G and beyond / Yong Zeng, Ismail Guvenc, Rui

  Zhang, Giovanni Geraci, David W. Matolak.

Description: Hoboken, NJ : Wiley‐IEEE Press, [2021] | Includes

  bibliographical references and index.

Identifiers: LCCN 2020030506 (print) | LCCN 2020030507 (ebook) | ISBN

  9781119575696 (hardback) | ISBN 9781119575672 (adobe pdf) | ISBN

  9781119575726 (epub)

Subjects: LCSH: Drone aircraft–Control systems. |

  Aeronautics–Communication systems. | Mobile communication systems. | 5G

  mobile communication systems.

Classification: LCC TL589.4 .Z465 2021 (print) | LCC TL589.4 (ebook) |

  DDC 629.135/5–dc23

LC record available at https://lccn.loc.gov/2020030506

LC ebook record available at https://lccn.loc.gov/2020030507

Cover Design: Wiley

Cover Image: © Waitforlight/Getty Images

List of Contributors

Rafhael Medeiros de Amorim

Nokia Bell Labs

Denmark

Chethan Kumar Anjinappa

Department of Electrical and Computer Engineering

North Carolina State University

NC

USA

M. Mahdi Azari

Department of Electrical Engineering

KU Leuven

Belgium

Morteza Banagar

Wireless@VT

Bradey Department of Electrical and Computer Engineering

Virginia Tech

Blacksburg

VA

USA

Arupjyoti Bhuyan

Idaho National Laboratory

Idaho Falls

ID

USA

Vishnu V. Chetlur

Wireless@VT

Bradey Department of Electrical and Computer Engineering

Virginia Tech

Blacksburg

VA

USA

Huaiyu Dai

Department of Electrical and Computer Engineering

North Carolina State University

NC

USA

Harpreet S. Dhillon

Wireless@VT

Bradey Department of Electrical and Computer Engineering

Virginia Tech

Blacksburg

VA

USA

Fatih Erden

Department of Electrical and Computer Engineering

North Carolina State University

NC

USA

Martins Ezuma

Department of Electrical and Computer Engineering

North Carolina State University

NC

USA

Uwe‐Carsten Fiebig

Institute of Communications and Navigation

German Aerospace Center (DLR)

Wessling

Germany

Robert W. Heath

Electrical and Computer Engineering Department

University of Texas at Austin

USA

Lorenzo Galati Giordano

Nokia Bell Labs

Dublin

Ireland

Adrian Garcia‐Rodriguez

Nokia Bell Labs

Dublin

Ireland

Giovanni Geraci

Universitat Pompeu Fabra

Barcelona

Spain

Nuria González‐Prelcic

Electrical and Computer Engineering Department

University of Texas at Austin

USA

Ismail Guvenc

Department of Electrical and Computer Engineering

North Carolina State University

NC

USA

Tianwei Hou

School of Electronic and Information Engineering

Beijing Jiaotong University

PR China

Wahab Khawaja

Department of Electrical and Computer Engineering

North Carolina State University

NC

USA

Aldebaro Klautau

Computer and Telecommunication Engineering Department

Universidade Federal do Pará

Brazil

István Z. Kovács

Nokia Bell Labs

Denmark

Abhaykumar Kumbhar

Department of Electrical and Computer Engineering

Florida International University

Miami

USA

Liang Liu

Department of Electronic and Information Engineering

The Hong Kong Polytechnic University

Hong Kong

Yuanwei Liu

School of Electronic Engineering and Computer Science

Queen Mary University of London

UK

David López‐Pérez

Nokia Bell Labs

Dublin

Ireland

David W. Matolak

Department of Electrical Engineering

University of South Carolina

SC

USA

Helka‐Liina Määttänen

Ericsson Research

Finland

Kamesh Namuduri

University of North Texas

USA

Ozgur Ozdemir

Department of Electrical and Computer Engineering

North Carolina State University

NC

USA

Sofie Pollin

Department of Electrical Engineering

KU Leuven

Belgium

Fernando Rosas

Data Science Institute

Department of Brain Sciences

and Center for Complexity Science

Imperial College London

UK

Nadisanka Rupasinghe

Department of Electrical and Computer Engineering

North Carolina State University

NC

USA

and

DOCOMO Innovations, Inc.

Palo Alto

CA

USA

Cristian Rusu

LCSL

Istituto Italiano di Tecnologia (IIT)

Liguria

Italy

Nicolas Schneckenberger

Institute of Communications and Navigation

German Aerospace Center (DLR)

Wessling

Germany

Troels B. Sørensen

Aalborg University

Denmark

Xin Sun

School of Electronic and Information Engineering

Beijing Jiaotong University

PR China

Jeroen Wigard

Nokia Bell Labs

Denmark

Qingqing Wu

State Key Laboratory of Internet of Things for Smart City

University of Macau

China

Jie Xu

Future Network of Intelligence Institute (FNii) and School of Science and Engineering

The Chinese University of Hong Kong

Shenzhen

PR China

Yavuz Yapici

Department of Electrical and Computer Engineering

North Carolina State University

NC

USA

Chiya Zhang

School of Electronic and Information Engineering

Harbin Institute of Technology

Shenzhen

China

and

Peng Cheng Laboratory (PCL)

Shenzhen

China

Rui Zhang

Department of Electrical and Computer Engineering

National University of Singapore

Singapore

Wei Zhang

School of Electrical Engineering and Telecommunications

University of New South Wales

Sydney

Australia

Yong Zeng

National Mobile Communications Research Laboratory

Southeast University

China

and

Purple Mountain Laboratories

Jiangsu

China

Acronyms

3GPP

3rd/third generation partnership project

5G

5th/fifth generation

5pSE

5th/fifth percentile spectral efficiency

AA

air‐to‐air

AG

air‐to‐ground

AG‐HetNet

air–ground heterogeneous cellular network

ASE

area spectral efficiency

ASTA

arrivals see time averages

AWGN

additive white Gaussian noise

B5G

beyond 5th/fifth generation

b/s/Hz

bits per second per hertz

BER

bit error rate

BHCA

busy hour call attempts

BPP

binomial point process

BPSK

binary phase shift keying

BR

bandwidth reservation

BS

base station

BSs/km

2

base stations per square kilometer

b.u.

bandwidth unit(s)

BVLoS

beyond‐visual‐line‐of‐sight

BW

bandwidth

C2

command and control

CAC

call/connection admission control

CBP

call blocking probability(‐ies)

CCDF

complementary cumulative distribution function

CCS

centum call seconds

CDF

cumulative distribution function

CDTM

connection dependent threshold model

CE2R

curved Earth two‐ray

CFO

carrier frequency offset

CI

close‐in

CIR

channel impulse response

CNPC

control and non‐payload communications

CRE

cell range expansion

CS

complete sharing

CSF

coordinated radio subframe

CSI

channel state information

CTF

channel transfer function

CW

continuous wave

DBS

drone base station

DiffServ

differentiated services

DME

distance‐measuring equipment

DPP

Doppler power profile

DS

dual slope

DSB‐AM

double‐sideband amplitude modulation

DS‐SS

direct sequence spread spectrum

EMLM

Erlang multirate loss model

eICIC

enhanced inter‐cell interference coordination

erl

the Erlang unit of traffic‐load

FAA

federal aviation administration

FBMC

filter bank multicarrier

FCC

federal communications commission

FeICIC

further‐enhanced inter‐cell interference coordination

FI

floating intercept

FIFO

first in‐first out

FMCW

frequency‐modulated continuous wave

Freq.

frequency

FSPL

free‐space path loss

GA

genetic algorithm

GBSCM

geometrically based stochastic channel model

GMSK

Gaussian minimum shift keying

GPS

global positioning system

GS

ground station

GSa/s

gigasamples per second

GSM

global system for mobile communication

GUE

ground user / ground user equipment

HAP

high‐altitude platform

HD

high definition

HetNet

heterogeneous network

ICI

inter‐carrier interference

ICIC

inter‐cell interference coordination

IMPC

intermittent multipath component

Infs.

infrastructure

IS‐GBSCM

irregular‐shaped geometric‐based stochastic channel model

ITU

International Telecommunication Union

kbps

kilobits per second

LAP

low‐altitude platform

LDACS

L‐band digital aeronautical communications systems

LDPLM

log‐distance path‐loss model

LoS / LOS

line‐of‐sight

LTE

long‐term evolution

LUI

Lisbon University Institute

mAh

milli‐amp hour

Mbps

megabits per second

MBS

macro base station

mgf

moment generating function

MIMO

multiple input–multiple output

MISO

multiple input–single output

mmWave

millimeter wave

Mod. sig.

modulated signal

MOI

MBS cell of interest / macro base station cell of interest

MPC

multipath component

mph

miles per hour

MSK

minimum shift keying

MUE

MBS GUE / macro base station ground user equipment

N/A

not applicable / not available

NGSCM

non‐geometric stochastic channel model

NLoS / NLOS

non‐line‐of‐sight

OFDM

orthogonal frequency‐division multiplexing

OHPLM

Okumura–Hata path‐loss model

OLOS

obstructed line‐of‐sight

PAPR

peak‐to‐average‐power ratio

PBS

pico base station

PDF

probability density function

PDP

power delay profile

PG

path gain

pgfl

probability generating functional

PL

path loss

PLE

path‐loss exponent

PPP

Poisson point process

PRN

pseudo‐random number

PSC

public safety communications

PSD

power spectral density

QoS

quality of service

RED

random early detection

RF

radio frequency

RHS

Right hand side

RMa

rural macro

RMS‐DS

root‐mean‐square delay spread

RS‐GBSCM

regular‐shaped geometric‐based stochastic channel model

RSRP

reference symbol received power

RSRQ

reference signal receive quality

RSS

received signal strength

RSSI

received signal strength indicator

RTT

round‐trip time

r.v.

random variable(s)

RW

random walk

RWP

random waypoint

RX

receiver

Satel.

satellite

SDMA

space‐division multiple access

SE

spectral efficiency

SIMO

single input–multiple output

SINR

signal‐to‐interference‐plus‐noise ratio

SIR

signal‐to‐interference ratio

SIRO

service in random order

SISO

single input–single output

SNR

signal‐to‐noise ratio

TDL

tapped delay line

TDMA

time division multiple access

Terres.

terrestrial

TOA

time of arrival

TX

transmitter

UABS

unmanned aerial base station

UAS

unmanned aircraft system / unmanned aerial system

UAV

unmanned aerial vehicle

UDM

user‐dependent model

UE

user / user equipment

UIM

user‐independent model

UMa

urban macro

UMi

urban micro

UMTS

universal mobile telecommunications service

UOI

UABS cell of interest / unmanned aerial base station cell of interest

USF

uncoordinated radio subframe

UUE

UABS GUE / unmanned aerial base station ground user equipment

UWB

ultra‐wideband

V2V

vehicle‐to‐vehicle

Vehic.

vehicular

VHF

very high frequency

WSS

wide‐sense stationary

Part IFundamentals of UAV Communications

1Overview

Qingqing Wu1, Yong Zeng3,4, and Rui Zhang2

1 State Key Laboratory of Internet of Things for Smart City, University of Macau, China

2 Department of Electrical and Computer Engineering, National University of Singapore, Singapore

3 National Mobile Communications Research Laboratory, Southeast University, China

4 Purple Mountain Laboratories, Jiangsu, China

1.1 UAV Definitions, Classes, and Global Trend

Unmanned aerial vehicles (UAVs), also commonly known as drones, are aircraft piloted by remote control or embedded computer programs without a human on‐board. Historically, UAVs were mainly used in military applications deployed in hostile territory for remote surveillance and armed attack, to reduce pilot losses. In recent years, enthusiasm for using UAVs in civilian and commercial applications has skyrocketed, thanks to the advancement of UAVs' manufacturing technologies and their reducing cost, making them more easily accessible to the public. Nowadays, UAVs have found numerous applications in a proliferation of fields, such as aerial inspection, photography, precision agriculture, traffic control, search and rescue, package delivery, and telecommunications, among others. In June 2016, the US Federal Aviation Administration (FAA) released operational rules for routine civilian use of small unmanned aircraft systems (UASs) with aircraft weight less than 55 pounds (25 kg) [9]. In November 2017, the FAA further launched a national program, namely the “Drone Integration Pilot Program,” to explore the expanded use of drones, including beyond‐visual‐line‐of‐sight (BVLoS) flights, night‐time operations, and flights above people [6]. It is anticipated that these new guidelines and programs will spur the further growth of the global UAV industry in the coming years. The scale of the industry of UAVs is potentially enormous, with realistic predictions in the realm of 80 billion US dollars for the US economy alone, expected to create tens of thousands of new jobs within the next decade [1]. Therefore, UAVs have emerged as a promising technology to offer fertile business opportunities in the next decade.

There are many types of UAVs due to their numerous and diversified applications. UAVs can be practically sorted into different categories according to criteria such as functionality, weight/payload, size, endurance, wing configuration, control method, cruising range, flying altitude, maximum speed, and energy‐supplying method. For example, in terms of wing configuration, fixed‐wing and rotary‐wing UAVs are the two main types of UAVs that have been widely used in practice. Typically, fixed‐wing UAVs have higher maximum flying speed, greater payloads, and longer endurance as compared to rotary‐wing UAVs, while their disadvantages lie in their inability to hover and the fact that a runway or launcher is needed for take‐off/landing. In contrast, rotary‐wing UAVs are able to take off/land vertically and hover at prescribed locations. Such different characteristics of these two types of UAVs thus have a great impact on their suitable use cases. Another common UAV classification method is based on size. Table 1.1 summarizes several key characteristics of four typical UAVs based on their size. A more comprehensive classification has been provided in [13]. In general, selecting a suitable UAV type is crucial for accomplishing their mission efficiently, which needs to take into account their specifications as well as the requirements of practical applications.

Table 1.1 Characteristics of different types of UAVs. Source: From Fotouhi et al. [10].

Micro

Small

Medium

Large

Example model

Kogan Nano Drone

DJI Spreading Wings S900

Scout B‐330 helicopter

Predator B

Weight

16 g

3.3 kg

90 kg

2223 kg

Payload

N/A

4.9 kg

50 kg

1700 kg

Flying mechanism

Multi‐rotor

Multi‐rotor

Multi‐rotor

Fixed wing

Range

50–80 m

N/A

N/A

1852 km

Altitude

N/A

N/A

3 km

5 km

Endurance

6–8 min

18 min

180 min

1800 min

Maximum speed

N/A

57.6 km 

100 km 

482 km 

Power supply

160 mAh Li battery

12000 mAh LiPo battery

21 kW gasoline

712 kW 950 shaft horsepower turboprop engine

Application

Recreation

Professional aerial photography; suitable to carry cellular base stations or user equipment

Data acquisition, HD video live stream; can carry cellular base stations or user equipment

Reconnaissance, airborne surveillance,target acquisition

1.2 UAV Communication and Spectrum Requirement

An essential enabling technology of UAS is wireless communication. On the one hand, UAVs need to exchange safety‐critical information with various parties such as remote pilots, nearby aerial vehicles, and air traffic controllers, to ensure safe, reliable, and efficient flight operation. This is commonly known as control and non‐payload communication (CNPC) [11]. On the other hand, depending on their missions, UAVs may need to transmit and/or receive in a timely manner mission‐related data such as aerial images, high‐speed video, and data packets for relaying to/from various ground entities such as UAV operators, end‐users, or ground gateways. This is known as payload communication.

Enabling reliable and secure CNPC links is a necessity for the large‐scale deployment and wide usage of UAVs. The International Telecommunication Union (ITU) has classified the required CNPC to ensure safe UAV operations into three categories [11].

Communication for UAV command and control

: This includes the telemetry report (e.g., flight status) from the UAV to the ground pilot, the real‐time telecommand signaling from ground to UAVs for non‐autonomous UAVs, and regular flight command update (such as waypoint update) for (semi‐)autonomous UAVs.

Communication for air traffic control (ATC) relay

: It is critical to ensure that UAVs do not cause any safety threat to traditional manned aircraft, especially for operations approaching areas with a high density of aircraft. To this end, a link between the air traffic controller and the ground control station via the UAV, called ATC relay, is required.

Communication supporting “sense and avoid”

: The ability to support “sense and avoid” ensures that the UAV maintains sufficient safety distance from nearby aerial vehicles, terrain, and obstacles.

The specific communication and spectrum requirements in general differ for CNPC and payload communications. Recently, the 3rd generation partnership project (3GPP) has specified the communication requirements for these two types of links [2], which are summarized in Table 1.2. CNPC is usually of low data rate, say, in the order of kilobits per second (kbps), but has rather stringent requirement on high reliability and low latency. For example, as shown in Table 1.2, the data rate requirement for UAV command and control is only in the range of 60–100 kbps for both downlink (DL) and uplink (UL) directions, but a reliability of less than packet error rate and a latency less than 50 ms (milliseconds) are required. While the communication requirements of CNPC links are similar for different types of UAVs due to their common safety considerations, those for payload data are highly application‐dependent. In Table 1.3, we list several typical UAV applications and their corresponding data communication requirements based on [4].

Table 1.2 UAV communication requirements specified by 3GPP. Source: Data from 3GPP TR 36.777 [2].

Data type

Data rate

Reliability

Latency

DL (ground station to UAV)

Command and control

60–100 kbps

packet error rate

50 ms

UL (UAV to ground station)

Command and control

60–100 kbps

packet error rate

N/A

Application data

Up to 50 Mbps

N/A

Similar to ground user

Table 1.3 Communication requirements for typical UAV applications. Source: Data from China mobile technical report  [4].

UAV application

Height coverage (m)

Payload traffic latency (ms)

Payload data rate (DL/UL)

Drone delivery

100

 500

300 kbps/200 kbps

Drone filming

100

 500

300 kbps/30 Mbps

Access point

500

 500

50 Mbps/50 Mbps

Surveillance

100

3000

300 kbps/10 Mbps

Infrastructure inspection

100

3000

300 kbps/10 Mbps

Drone fleet show

200

 100

200 kbps/200 kbps

Precision agriculture

300

 500

300 kbps/200 kbps

Search and rescue

100

 500

300 kbps/6 Mbps

Since the loss of CNPC link may cause catastrophic consequences, the International Civil Aviation Organization (ICAO) has determined that CNPC links for UAVs must operate over protected aviation spectrum [8,12]. Furthermore, ITU studies have revealed that, to support CNPC for the forecast number of UAVs in coming years, 34 MHz (megahertz) terrestrial spectrum and 56 MHz satellite spectrum are needed for supporting both line‐of‐sight (LoS) and beyond‐LoS UAV operations [11]. To meet such requirements, the C‐band spectrum at 5030–5091 MHz was made available for UAV CNPC at the 2012 World Radiocommunication Conference (WRC‐12). More recently, the WRC‐15 has decided that geostationary Fixed Satellite Service (FSS) networks may be used for UAS CNPC links.

Compared to CNPC, UAV payload communication usually has a much higher data rate requirement. For instance, to support the transmission of full high‐definition (FHD) video from the UAV to the ground user, the transmission rate is about several megabits per second (Mbps), while for 4K video, it is higher than 30 Mbps. The rate requirement for UAV serving as an aerial communication platform can be even higher, e.g., up to dozens of gigabits per second (Gbps) for data forwarding/backhauling applications.

1.3 Potential Existing Technologies for UAV Communications

To support the CNPC and payload communication in multifarious UAV applications, proper wireless technologies need to be selected for achieving seamless connectivity and high reliability/throughput for both air‐to‐air and air‐to‐ground wireless communications in 3D space. Towards this end, four candidate communication technologies are listed and compared in Table 1.4, including (i) direct link, (ii) satellite, (iii) ad‐hoc network, and (iv) cellular network.

Table 1.4 Comparison of wireless technologies for UAV communication.

Technology

Description

Advantages

Disadvantages

Direct link

Direct point‐to‐point communication with ground node

Simple, low cost

Limited range, low data rate, vulnerable to interference, non‐scalable

Satellite

Communication and Internet access via satellite

Global coverage

Costly, heavy/bulky/energy‐ consuming communication equipment, high latency, large signal attenuation

Ad‐hoc network

Dynamically self‐organizing and infrastructure‐free network

Robust and adaptable, support for high mobility

Costly, low spectrum efficiency, intermittent connectivity, complex routing protocol

Cellular network

Enabling UAV communications by using cellular infrastructure and technologies

Almost ubiquitous accessibility, cost‐effective, superior performance and scalability

Unavailable in remote areas, potential interference with terrestrial communications

1.3.1 Direct Link

Due to its simplicity and low cost, the direct point‐to‐point communication between a UAV and its associated ground node over the unlicensed band (e.g., the Industrial Scientific Medical (ISM) 2.4 GHz band) was most commonly used for commercial UAVs in the past, where the ground node can be a joystick, remote controller, or ground station. However, it is usually limited to LoS communication, which significantly constrains its operation range and hinders its applications in complex propagation environments. For example, in urban areas, the communication can be easily blocked by, e.g., trees and high‐rise buildings, which results in poor reliability and low data rate. In addition, such a simple solution is usually insecure and vulnerable to interference and jamming. Due to the above limitations, the simple direct‐link communication is not a scalable solution for supporting large‐scale deployment of UAVs in the future.

1.3.2 Satellite

Enabling UAV communications by leveraging satellites is a viable option due to their global coverage. Specifically, satellites can help relay data communicated between widely separated UAVs and ground gateways, which is particularly useful for UAVs above oceans and in remote areas where terrestrial network (WiFi or cellular) coverage is unavailable. Furthermore, satellite signals can also be used for navigation and localization of UAVs. In WRC‐15, the conditional use of satellite communication frequencies in the Ku/Ka band has been approved to connect drones to satellites, and some satellite companies such as Inmarsat have launched a satellite communication service for UAVs [5]. However, there are also several disadvantages of satellite‐enabled UAV communications. First, the propagation loss and delay are quite significant due to the long distances between satellite and low‐altitude UAVs/ground stations. This thus poses great challenges for meeting ultra‐reliable and delay‐sensitive CNPC for UAVs. Second, UAVs usually have stringent size, weight, and power (SWAP) constraints, and thus may not be able to carry the heavy, bulky, and energy‐consuming satellite communication equipment (e.g., dish antenna) required. Third, the high operational cost of satellite communication also hinders its wide use for densely deployed UAVs in consumer‐grade applications.

1.3.3 Ad‐Hoc Network

Mobile ad‐hoc network (MANET) is an infrastructure‐free and dynamically self‐organizing network for enabling peer‐to‐peer communications among mobile devices such as laptops, cellphones, and walkie‐talkies. Such devices usually communicate over bandwidth‐constrained wireless links using, e.g., IEEE 802.11 a/b/g/n. Each device in a MANET can move randomly over time; as a result, its link conditions with other devices may change frequently. Furthermore, for supporting communications between two far‐apart nodes, some other nodes in between need to help forward the data via multi‐hop relaying, thus incurring more energy consumption, low spectrum efficiency, and long end‐to‐end delay. Vehicular ad‐hoc network (VANET) and flying ad‐hoc network (FANET) are two applications of MANET, for supporting communications among high‐mobility ground vehicles and UAVs in 2D and 3D networks, respectively [7].

The topology or configuration of a FANET for UAVs may take different forms, such as a mesh, ring, star, or even a straight line, depending on the application scenario. For example, a star network topology is suitable for UAV swarm applications, for which UAVs in a swarm all communicate through a central hub UAV that is responsible for communicating with the ground stations. Although FANET is a robust and flexible architecture for supporting UAV communications in a small network, it is generally unable to provide a scalable solution for serving massive UAVs deployed in a wide area, due to the complexities and difficulties for realizing a reliable routing protocol over the whole network with dynamic and intermittent link connectivities among the flying UAVs.

1.3.4 Cellular Network

It is evident that the aforementioned technologies generally cannot support large‐scale UAV communications in a cost‐effective manner. On the other hand, it is also economically nonviable to build new and dedicated ground networks for achieving this goal. As such, there has been significantly growing interest recently in leveraging the existing as well as future‐generation cellular networks for enabling UAV–ground communications [17]. Thanks to the almost ubiquitous coverage of the cellular network worldwide as well as its high‐speed optical backhaul and advanced communication technologies, both CNPC and payload communication requirements for UAVs can be potentially met, regardless of the density of UAVs as well as their distances from the corresponding ground nodes. For example, the forthcoming fifth‐generation (5G) cellular network is expected to support a peak data rate of 10 Gbps with only 1 ms round‐trip latency, which in principle is adequate for high‐rate and delay‐sensitive UAV communication applications such as real‐time video streaming and data relaying.

Figure 1.1 Supporting UAV communications with an integrated network architecture. Source: From Zeng et al. [19].

Despite the promising advantages of cellular‐enabled UAV communications, there are still scenarios where the cellular services are unavailable, e.g., in remote areas such as sea, desert, and forest. In such scenarios, other technologies such as direct link, satellite, and FANET can be used to support UAV communications beyond the terrestrial coverage of cellular networks. Therefore, it is envisioned that the future wireless network for supporting large‐scale UAV communications will have an integrated 3D architecture consisting of UAV‐to‐UAV, UAV‐to‐satellite, and UAV‐to‐ground communications, as shown in Figure 1.1, where each UAV may be enabled with one or more communication technologies to exploit the rich connectivity diversity in such a hybrid network.

1.4 Two Paradigms in Cellular UAV Communications

In this section, we further discuss the aforementioned new paradigm of integrating UAVs into the cellular network, to provide their full horizon of applications and benefits. In particular, we partition our discussion into two main categories. On the one hand, UAVs are considered as new aerial users that access the cellular network from the sky for communications, which we refer to as cellular‐connected UAVs. On the other hand, UAVs are used as new aerial communication platforms such as base stations (BSs) and relays, to assist in terrestrial wireless communications by providing data access from the sky, thus called UAV‐assisted wireless communications.

1.4.1 Cellular‐Connected UAVs

By incorporating UAVs as new user equipment in the cellular network, the following benefits can be achieved [17]. First, thanks to the almost worldwide accessibility of cellular networks, a cellular‐connected UAV makes it possible for the ground pilot to remotely command and control the UAV with virtually unlimited operation range. Besides, it also provides an effective solution to maintain wireless connectivity between UAVs and various other stakeholders, such as the end‐users and air traffic controllers, regardless of their locations. This thus opens up many new UAV applications in the future.

Second, with the advanced cellular technologies and authentication mechanisms, a cellular‐connected UAV is expected to achieve significant performance improvement over the other technologies introduced in Section 1.3, in terms of reliability, security, and data throughput. For instance, the current fourth‐generation (4G) long‐term evolution (LTE) cellular network employs a scheduling‐based channel access mechanism, where multiple users can be served simultaneously by assigning them orthogonal resource blocks (RBs). In contrast, WiFi (e.g., 802.11g employed in FANET) adopts contention‐based channel access with a random backoff mechanism, where users are allowed to access only channels that are sensed to be idle. Thus, multiuser transmission with centralized scheduling/control enables the cellular network to make a more efficient use of the spectrum than WiFi, especially when the user density is high. In addition, UAV‐to‐UAV communication can also be realized by leveraging the available device‐to‐device (D2D) communications in LTE and 5G systems.

Third, a cellular‐assisted localization service can provide UAVs with a new and complementary means in addition to the conventional satellite‐based global positioning system (GPS) for achieving more robust UAV navigation performance. Last, but not least, a cellular‐connected UAV is a cost‐effective solution since it reuses the millions of cellular BSs worldwide without the need to build new infrastructure dedicated for UAS only. Thus, the cellular‐connected UAV is expected to be a win–win technology for both UAV and cellular industries, with rich business opportunities to explore in the future.

1.4.2 UAV‐Assisted Wireless Communications

Thanks to the continuous cost reduction in UAV manufacturing and device miniaturization in communication equipment, it has become more feasible to mount compact and small‐size BSs or relays on UAVs to enable flying aerial platforms to assist in terrestrial wireless communications. For instance, commercial LTE BSs with light weight (e.g., less than 4 kg) are already available in the market, which are suitable to be mounted on UAVs with moderate payload.

Compared to conventional terrestrial communications with typically static BSs/relays deployed at fixed locations, UAV‐assisted communications bring the following main advantages [21]. First, UAV‐mounted BSs/relays can be swiftly deployed on demand. This is especially appealing for application scenarios such as temporary or unexpected events, emergency response, and search and rescue, among others. Second, thanks to their high altitude above the ground, UAV BSs/relays are more likely to have LoS connection with their ground users as compared to their terrestrial counterparts, thus providing more reliable links for communication as well as multiuser scheduling and resource allocation. Third, thanks to the controllable high mobility of UAVs, UAV BSs/relays possess an additional degree of freedom (DoF) for communication performance enhancement, by dynamically adjusting their locations in 3D to cater for the terrestrial communication demands.

For 5G wireless networks, the three most representative commercial scenarios are enhanced mobile broadband (eMBB), massive machine‐type communications (mMTC), and ultra‐reliable and low‐latency communications (URLLC) (also known as mission‐critical communications), which are particularly appealing for UAV communications. Specifically, eMBB supports reliable connections with very high peak data rates, as well as moderate rates for cell‐edge users; mMTC supports a massive number of Internet‐of‐Things (IoT) devices, which are only sporadically active and send small data payloads; and URLLC supports low‐latency transmissions of small payloads with very high reliability from a limited set of terminals, which are active according to patterns typically specified by outside events, such as alarms. As such, the advantages of UAV‐assisted communication make it a promising technology to support the main 5G applications with ever‐increasing and highly dynamic wireless data traffic.

1.5 New Opportunities and Challenges

The integration of UAVs into cellular networks, as either aerial users or communication platforms, brings new design opportunities as well as challenges. Both cellular‐connected UAV communication and UAV‐assisted wireless communication are significantly different from their terrestrial counterparts, due to the high altitude and high mobility of UAVs, the high probability of UAV–ground LoS channels, the distinct communication quality of service (QoS) requirements for CNPC versus mission‐related payload data, the stringent SWAP constraints of UAVs, as well as the new design DoF by jointly exploiting the UAV mobility control and communication scheduling/resource allocation. Table 1.5 summarizes the main design opportunities and challenges of cellular communications with UAVs, which are further elaborated as follows.

Table 1.5 New opportunities and challenges in UAV communications.

Characteristic

Opportunities

Challenges

High altitude

Wide ground coverage as aerial BS/relay

Requires 3D cellular coverage for aerial user

High LoS probability

Strong and reliable communication link; high macro‐diversity; slow communication scheduling and resource allocation

Severe aerial–terrestrial interference; susceptible to terrestrial jamming/eavesdropping

High 3D mobility

Traffic‐adaptive movement; QoS‐aware trajectory design

Handover management; wireless backhaul

SWAP constraint

N/A

Limited payload and endurance; energy‐efficient design; compact and lightweight BS/relay and antenna design

1.5.1 High Altitude

Compared with conventional terrestrial BSs/users, UAV BSs/users usually have much higher altitude. For instance, the typical height of a terrestrial BS is around 10 m for Urban Micro (UMi) deployment and 25 m for Urban Macro (UMa) deployment [2], whereas the current regulation already allows UAVs to fly up to 122 m [9]. For cellular‐connected UAVs, the high UAV altitude requires cellular BSs to offer 3D aerial coverage for UAV users, in contrast to the conventional 2D coverage for terrestrial users. However, existing BS antennas are usually tilted downwards, either mechanically or electronically, to cater for the ground coverage as well as suppressing inter‐cell interference. Nevertheless, preliminary field measurement results have