![viii Contents
6.6.1 Communication Constraints and Objective 107
6.7 Association of SCBs with UAVs 108
6.8 Results and Discussions 110
6.8.1 Analysis of Results 110
6.9 Conclusion 114
Bibliography 115
7 UAV-Enabled Cooperative Jamming for Physical Layer
Security in Cognitive Radio Network 119
Phu X. Nguyen, Hieu V. Nguyen, Van-Dinh Nguyen, and Oh-Soon
Shin
7.1 Introduction 119
7.2 System Model 121
7.2.1 Signal Model 121
7.2.2 Optimization Problem Formulation 125
7.3 Proposed Algorithm 125
7.3.1 Tractable Formulation for the Optimization Problem P2 126
7.3.1.1 Tractable Formulation for RS[n] 126
7.3.1.2 Tractable Formulation for RE[n] 127
7.3.1.3 Tractable Formulation for Constraint (7.10i) 127
7.3.1.4 Safe Optimization Problem 128
7.3.2 Proposed IA-Based Algorithm 128
7.4 Numerical Results 133
7.5 Conclusion 136
Bibliography 138
8 IRS-Assisted Localization for Airborne Mobile
Networks 141
Olaoluwa Popoola, Shuja Ansari, Rafay I. Ansari, Lina Mohjazi, Syed
A. Hassan, Nauman Aslam, Qammer H. Abbasi, and Muhammad
A. Imran
8.1 Introduction 141
8.1.1 Related Work 143
8.1.2 Unmanned Aerial Vehicles 143
8.1.3 Intelligent Reflecting Surface 143
8.2 Intelligent Reflecting Surfaces in Airborne Networks 144
8.2.1 Aerial Networks with Integrated IRS 145
8.2.1.1 Integration of IRS in High-Altitude Platform Stations (HAPSs)
for Remote Areas Support 145](https://image.slidesharecdn.com/18691482-250606172102-9f13893a/85/Autonomous-Airborne-Wireless-Networks-Ieee-Press-1st-Edition-Muhammad-Ali-Imran-Editor-14-320.jpg)
![7
2
Channel Model for Airborne Networks
Aziz A. Khuwaja1,2
and Yunfei Chen1
1
School of Engineering, Electrical and Electronic Engineering Stream, University of Warwick,
Coventry, UK
2Department of Electrical Engineering, Sukkur IBA University, Sukkur, Pakistan
2.1 Introduction
The use of unmanned aerial vehicles (UAVs) is desirable due to their
high maneuverability, ease of operability, and affordable prices in various
civilian applications, such as disaster relief, aerial photography, remote
surveillance, and continuous telemetry. One of the promising application of
UAVs is enabling the wireless communication network in cases of natural
calamity and in hot spot areas during peak demand where the resources of
the existing communication network have been depleted [1]. Qualcomm
has already initiated field trials for the execution of fifth generation (5G)
cellular applications [2]. Google and Facebook are also exploiting the use of
UAVs to provide Internet access to far-flung destinations [3].
The selection of an appropriate type of UAV is essential to meet the desired
quality of service (QoS) depending on applications and goals in different
environments. In fact, for any specific wireless networking application,
the UAV altitude and its capabilities must be taken into account. UAVs can
be categorized, based on their altitude, into low-altitude platforms (LAPs)
and high-altitude platforms (HAPs). Furthermore, based on their structure,
UAVs can be categorized as fixed-wing and rotary-wing UAVs. In compar-
ison with rotary wings, fixed-wing UAVs move in the forward direction to
remain aloft, whereas rotary-wing UAVs are desired for applications that
require UAVs to be quasi-stationary over a given area. However, in both
types, flight duration depends on their energy sources, weight, speed, and
trajectory.
Autonomous Airborne Wireless Networks, First Edition.
Edited by Muhammad Ali Imran, Oluwakayode Onireti, Shuja Ansari, and Qammer H. Abbasi.
© 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.](https://image.slidesharecdn.com/18691482-250606172102-9f13893a/85/Autonomous-Airborne-Wireless-Networks-Ieee-Press-1st-Edition-Muhammad-Ali-Imran-Editor-33-320.jpg)
![2.2 UAV Classification 9
Table 2.1 Regulation for LAP deployment of UAVs in different countries.
Country
Maximum
altitude (m)
Minimum distance
to humans (m)
Minimum distance
to airport (km)
US 122 — 8
UK 122 50 —
Chile 130 36 —
Australia 120 30 5.5
South Africa 46 50 10
altitude. In addition, for any particular UAV-enabled wireless networking
application, several factors, such as the number of UAVs, their optimal
deployment, and QoS requirement, must be taken in to account. The
operational altitude of the UAV from the ground level can be categorized as
LAP and HAP. UAVs in LAP can fly between the altitude ranges from tens
of meters to a few kilometers [4]. However, civil aviation authorities of some
countries have set the operational altitude of UAVs up to a few hundred
meters to avoid airborne collision with commercial flights. For example,
Table 2.1 lists the regulations of maximum allowable LAP deployment
of UAVs in various countries without any specific permit [5]. HAPs, on
the other hand, have altitudes above 17 km where UAVs are typically
quasi-stationary [1, 4].
For time-sensitive applications such as emergency services, LAPs are
more appropriate then HAPs due to their rapid deployment, quick mobility,
and cost-effectiveness. Furthermore, LAPs can be used for collecting sensor
data from the ground. In this case, LAPs can be readily replaced or recharged
as needed. In contrast, HAPs are preferred due to their long endurance (days
or months) operations and wider ground coverage [1]. However, operational
cost of HAPs is high and their deployment time is significantly longer.
UAV can also be categorized based on their structure into rotary-wing and
fixed-wing UAVs. Rotary-wing UAVs are powered by rotating blades, and
based on the number of blades they are termed as either quadcopter with
four blades, hexacopter with six blades, or octocopter with eight blades. On
the other hand, fixed-wing UAVs include those that are driven by propellers
with small size engine and have wings that are fixed. However, the flight
time of UAVs relies on several key factors, such as type, weight, speed, energy
sources (battery or engine), and trajectory of the UAV.](https://image.slidesharecdn.com/18691482-250606172102-9f13893a/85/Autonomous-Airborne-Wireless-Networks-Ieee-Press-1st-Edition-Muhammad-Ali-Imran-Editor-35-320.jpg)
![2.4 Channel Modeling in UAV Communications 13
on their respective random phases [6]. Typically, several fading mechanisms
are added linearly in dB to represent the radio channel as
y = PL + XL + XS, (2.1)
where PL is the distance-dependent path loss, XL is the large-scale fading
consisting of power variation on a large scale due to the environment, and
XS is the small-scale fading. Parameters of channel model, such as path loss
exponent and LoS probability, are dependent on the altitude level because
propagation conditions change at different altitudes. The airspace is often
segregated into three propagation echelons or slices as follows:
● Terrestrial channel: For suburban and urban environments, altitude
is between 10 and 22.5 m, respectively [7]. In this case, the terrestrial
channel models can be used to model AG propagation because the air-
borne UAV is below the rooftop level. As a result, NLoS is the dominant
component in the propagation.
● Obstructed AG channel: For suburban and urban environments, altitude
is 10–40 m and 22.5–100 m, respectively. In this case, LoS probability is
higher than that of the terrestrial channels.
● High-altitude AG channel: All channels are in LoS for the altitude ranges
between 100 and 300 m or above. Consequently, the propagation is similar
to that in the free space case. Moreover, no shadowing is experienced for
these channels.
2.4.1.1 Path Loss and Large-Scale Fading
Air-to-Air Channel Free space path loss model is the simplest channel model
to represent the AA propagation at a relatively high altitude. Thus, the
received power is given by [6]
PR = PTGTGR
(
𝜆c
4𝜋d
)2
, (2.2)
where PT denotes the transmitted signal power, GT and GR represent the gain
of the transmitter and receiver antennas, respectively, d is the ground dis-
tance between the transmitter and receiver, and 𝜆c is the carrier wavelength.
Path loss exponent (𝜂) is the rate of distance-dependent power loss, where
𝜂 varies with environments. In Eq. (2.2), 𝜂 = 2 for free space propagation.
Therefore, the distance-dependent path loss expression can be generalized
as
PL =
(
4𝜋d
𝜆c
)𝜂
. (2.3)](https://image.slidesharecdn.com/18691482-250606172102-9f13893a/85/Autonomous-Airborne-Wireless-Networks-Ieee-Press-1st-Edition-Muhammad-Ali-Imran-Editor-39-320.jpg)
![14 2 Channel Model for Airborne Networks
Air-to-Ground Channel In urban environment, the AG channel may not expe-
rience complete free space propagation. In the existing literature on UAV
communications, the log-distance model is the prominently used path loss
model due to its simplicity and applicability when environmental parame-
ters are difficult to define. Therefore, path loss in dB is given by
PL(d) = PL0 + 10𝜂 log
(
d
d0
)
, (2.4)
where PL0 = 20 log
(
4𝜋d0
𝜆c
)
is the path loss for the reference distance d0.
For the same propagation distance between the ground device and the UAV,
large-scale variations are different at different locations within the same
environment because the materials of obstacles vary from each other, which
affects the radio signal propagation. As a result, at any distance d, XL in
Eq. (2.1) is the shadow fading measured in dB and modeled as the normal
random variable with variance 𝜎 in dB. This model is extensively applied
for modeling of the terrestrial channels. Table 2.2 lists some measurement
campaigns for the estimations of path loss and large-scale effects.
Another popular channel model to characterize the AG propagation in
UAV communications is the probabilistic path loss model in [4] and [17]. In
[17], the path loss between the ground device and the UAV is dependent on
the position of the UAV and the propagation environments (e.g. suburban,
urban, dense-urban, high-rise). Consequently, during the AG radio propa-
gation, the communication link can be either LoS or NLoS depending on
the environment. Many of the existing works [18–35] on UAV communi-
cations adopted the probabilistic path loss model of [4] and [17]. In these
works, the probability of occurrence of LoS and NLoS links are functions
of the environmental parameters, height of the buildings, and the elevation
angle between the ground device and the UAV. This model is based on envi-
ronmental parameters defined in the recommendations of the International
Telecommunication Union (ITU). In particular, ITU-R provides statistical
parameters related to the environment that determine the height, number,
and density of the buildings or obstacles. For instance, in [36], the height of
the buildings can be modeled by using the Rayleigh distribution. The aver-
age path loss for the AG propagation in [17] is given as
PL = ℙLoS × PLLoS +
(
1 − ℙLoS
)
× PLNLoS, (2.5)
where PLLoS and PLNLoS are the LoS and NLoS path loss, respectively, for the
free space propagation. ℙLoS is the LoS probability given as
ℙLoS =
1
1 + e−(𝜃−)
, (2.6)](https://image.slidesharecdn.com/18691482-250606172102-9f13893a/85/Autonomous-Airborne-Wireless-Networks-Ieee-Press-1st-Edition-Muhammad-Ali-Imran-Editor-40-320.jpg)
![Table 2.2 Measurement campaigns to characterize the path loss and large-scale AG propagation fading.
References Scenario 𝜼 PL0 (dB) 𝝈 (dB)
Yanmaz et al. [8] Urban/Open field 2.2–2.6 — —
Yanmaz et al. [9] Open field 2.01 — —
Ahmed et al. [10] — 2.32 — —
Khawaja et al. [11] Suburban/Open field 2.54–3.037 21.9–34.9 2.79–5.3
Newhall et al. [12] Urban/Rural 4.1 — 5.24
Tu and Shimamoto [13] Near airports 2–2.25 — —
Matolak and Sun [14] Suburban 1.7 (L-band) 98.2–99.4 (L-band) 2.6–3.1 (L-band)
1.5–2 (C-band) 110.4–116.7 (C-band) 2.9–3.2 (C-band)
Sun and Matolak [15] Mountains 1–1.8 96.1–123.9 2.2–3.9
Meng and Lee [16] Over sea 1.4–2.46 19–129 —](https://image.slidesharecdn.com/18691482-250606172102-9f13893a/85/Autonomous-Airborne-Wireless-Networks-Ieee-Press-1st-Edition-Muhammad-Ali-Imran-Editor-41-320.jpg)
![16 2 Channel Model for Airborne Networks
where and are the constant values related to the environment,
𝜃 = arctan
(
h
d
)
is the elevation angle between the ground user and the
UAV, h is the altitude of the UAV, and d is the distance between the ground
projection of the UAV and the ground device. According to Eq. (2.6), as
the elevation angle increases with the UAV altitude, the blockage effect
decreases and the AG propagation becomes more LoS. An advantage of this
model is that it is applicable for different environments and for different
UAV altitudes. However, it is unable to capture the impact of path loss for
AG propagation in mountainous regions and over water bodies due to the
lack of information related to their statistical parameters.
Conventional well-known channel models for cellular communications
can be used for UAV communications for UAV altitude between 1.5 and
10 m. One such model for the macro-cell network was designed for the rural
environment by the 3rd Generation Partnership Project (3GPP) in [7, 37].
Since LoS and NLoS links are treated separately, the probability of LoS
propagation is expressed as
ℙG
LoS =
{
1, if d ≤ 10 m,
e− d−10
1000 , if 10 m < d.
(2.7)
Path loss and large-scale fading can be calculated once the LoS probability
is known from Eq. (2.7). As the communication nodes change their position,
path loss also changes and can be found as
PLG
LoS =
{
PLG
1 , if 10 m ≤ d ≤ ̂
d,
PLG
2 , if ̂
d ≤ d ≤ 10 km,
(2.8)
PLG
NLoS = max
(
PLG
LoS, ̂
PL
G
NLoS
)
, for 10 m ≤ d ≤ 5 km, (2.9)
where
PLG
1 = 20 log
(
40𝜋dfc
3
)
+ min (0.03h1.72
, 10) log(d)
− min (0.44h1.72, 14.77) + 0.002d log(h),
(2.10)
PLG
2 = PLG
1 + 40 log
(
d
̂
d
)
, (2.11)
̂
PL
G
NLoS =161.04 − 7.1 log(𝑤) + 7.5 log(h) −
(
24.37 − 3.7
(
h
hG
)2
)
log(hG)
+
(
43.42 − 3.1 log
(
hG
)) (
log(d) − 3
)
+ 20 log( fc)
−
(
3.2 log (11.75h)2
− 4.97
)
, (2.12)
̂
d = 2𝜋hhG
fc
c
, (2.13)](https://image.slidesharecdn.com/18691482-250606172102-9f13893a/85/Autonomous-Airborne-Wireless-Networks-Ieee-Press-1st-Edition-Muhammad-Ali-Imran-Editor-42-320.jpg)
![2.4 Channel Modeling in UAV Communications 17
with fc, hG, 𝑤, and c being the carrier frequency, height of ground BS, the
average width of street, and the speed of light, respectively.
For the obstructed AG propagation with the UAV altitude between 10 and
40 m, the LoS probability in the rural environment for the macro-cell net-
work can be computed as [7]
ℙA
LoS =
⎧
⎪
⎨
⎪
⎩
1, if d ≤ ̃
d,
̃
d
d
+ e
(
−d
p1
)(
1− −̃
d
d
)
, if ̃
d < d,
(2.14)
where
̃
d = max
(
1350.8 log(h) − 1602, 18
)
, (2.15)
p1 = max
(
15021 log(h) − 160 53, 1000
)
. (2.16)
The path loss for LoS and NLoS links can be computed as
PLA
LoS = max
(
23.9 − 1.8 log(h), 20
)
log(d) + 20 log
(
40𝜋fc
3
)
, (2.17)
PLA
NLoS = max
(
PLA
LoS, −12 + (35 − 5.3 log(h)) log(d) + 20 log
(
40𝜋fc
3
))
.
(2.18)
For a high-altitude AG channel with 40 m < h ≤ 300 m, the LoS probabil-
ity is 1 and the path loss can be formulated as Eq. (2.17).
2.4.1.2 Small-Scale Fading
Small-scale fading refers to the random fluctuations of amplitude and phase
of the received signal over a short distance or a short period of time due to
constructive or destructive interference of the MPC. For different propaga-
tion environments and wireless systems, different distribution models are
suggested to analyze the random variations in the received signal envelop.
The Rician and Rayleigh distributions are widely used models in the
literature of wireless communications, where both are based on the central
limit theorem. The Rician distribution provides better fit for the AA and AG
channels, where the impact of LoS propagation is stronger. On the other
hand, when the MPC impinges at the receiver with random amplitude
and phase, the small-scale fading effect can be captured by the Rayleigh
distribution [6].
Geometrical analysis, numerical simulations, and empirical data are used
to obtain the stochastic fading models [38–40]. Geometry-based stochastic](https://image.slidesharecdn.com/18691482-250606172102-9f13893a/85/Autonomous-Airborne-Wireless-Networks-Ieee-Press-1st-Edition-Muhammad-Ali-Imran-Editor-43-320.jpg)
![18 2 Channel Model for Airborne Networks
Table 2.3 Measured small-scale fading of AG propagation in different
environments.
References Scenario
Frequency
band
Fading
distribution
Khawaja et al. [11] Suburban/Open field Ultra-wideband Nakagami
Newhall et al. [12] Urban/Suburban Wideband Rayleigh,
Rician
Tu and
Shimamoto [13]
Urban/Suburban Wideband Rician
Matolak and
Sun [14]
Urban/Suburban Wideband Rician
Simunek et al. [45] Urban/Suburban Narrowband Rician
Cid et al. [46] Forest/Foliage Ultra-wideband Rician,
Nakagami
Matolak and
Sun [47]
Sea/Fresh water Wideband Rician
channel model (GBSCM) is the most popular type of small-scale fading
model. GBSCM is subdivided into regular-shaped geometry-based stochas-
tic channel model (RS-GBSCM) and irregular-shaped geometry-based
stochastic channel model (IS-GBSCM). Time-variant IS-GBSCM was pre-
sented in [41] and RS-GBSCM was presented in [42] and [43].These works
illustrated Rician distribution for small-scale fading. In [44], non-geometric
stochastic channel model (NGSCM) was provided, where small-scale
effects of AG propagation were modeled by using Rician and Loo models.
Table 2.3 provides the measured characteristics of small-scale fading of AG
propagation in different environments.
2.4.1.3 Airframe Shadowing
Airframe shadowing occurs when the LoS of AG propagation is obstructed
by the UAV structure. This impairment is unique to UAV communications
for both AA and AG channels and does not exist in conventional cellular
communications. Airframe shadowing is more severe in fixed-wing UAVs
mounted with single antenna. In this case, the AG communication link can
be severe during roll, pitch, or yaw motion of the UAV. One possible solution
to alleviate airframe shadowing is to replace the single-antenna system
with spatially separated multiple antennas. Other factors responsible for
airframe shadowing are the size, shape, and material of the UAV. The
seminal work on the measurement of airframe shadowing was performed](https://image.slidesharecdn.com/18691482-250606172102-9f13893a/85/Autonomous-Airborne-Wireless-Networks-Ieee-Press-1st-Edition-Muhammad-Ali-Imran-Editor-44-320.jpg)
![2.5 Key Research Challenges of UAV-Enabled Wireless Network 19
in [48], which found that the aircraft roll angle was proportional to the shad-
owing attenuation. Moreover, shadowing duration depends on the flight
maneuvering.
2.5 Key Research Challenges of UAV-Enabled
Wireless Network
This section discusses some of the key research challenges for the practical
deployment of UAVs as airborne wireless nodes.
2.5.1 Optimal Deployment of UAVs
In UAV-based communications, one of the key challenges is the optimal
three-dimensional deployment of hovering UAV. The capability of UAV to
maneuver and adjust its altitude provides additional degree of freedom for
UAV deployment in an efficient manner to improve capacity and coverage.
In fact, UAV deployment is more challenging in UAV communications than
in conventional terrestrial communications because the characteristics of
AG propagation change with the position of the UAV. However, for efficient
UAV deployment, flight duration and energy constraints must be taken
into account for battery-operated UAV, as they affect the performance of
networks. In addition, simultaneous deployment of multiple UAVs is more
challenging because of the co-channel interference and the possibility of
airborne collision of UAVs. Another important issue is the UAV deploy-
ment in the presence of terrestrial network. UAV deployment problem
has been extensively discussed in the literature for coverage maximization
[17, 29, 30, 33, 33], data collection from Internet of Things (IoT) devices [31],
UAV-assisted wireless network [27], disaster scenario [49], and caching
applications [22].
2.5.2 UAV Trajectory Optimization
Optimal trajectory design for mobile UAV is an important issue in
UAV-based communications. Specifically, optimal path planning is crucial
for UAVs operating for data collection from ground-based sensors and
caching scenarios. UAV trajectory planning is mostly effected by the dimen-
sion of the target area, flight duration of the mission, QoS requirement by
the ground users, and energy constraints. Apart from physical parameters,
UAV trajectory optimization is analytically a challenging problem because
it involves a fixed number of optimization variables related to the UAV](https://image.slidesharecdn.com/18691482-250606172102-9f13893a/85/Autonomous-Airborne-Wireless-Networks-Ieee-Press-1st-Edition-Muhammad-Ali-Imran-Editor-45-320.jpg)
![20 2 Channel Model for Airborne Networks
locations [1]. In addition, UAV trajectory optimization requires coupling
between different QoS metrics in wireless communication with the mobil-
ity of UAV. Recently, there have been a number of studies on the joint
trajectory optimization of UAV with its wireless communication metrics,
such as throughput maximization in [50–52] and energy-efficient UAV
communication in [53, 54].
2.5.3 Energy Efficiency and Resource Management
Energy efficiency and resource management require attention where UAVs
are operating in key scenarios to collect data from IoT devices, ensure pub-
lic safety, and support cellular wireless network. Resource management is
a major challenge in UAV communications unlike in cellular communica-
tions [55]. However, UAV communications introduce additional hindrance
in radio resource management due to the interplay between the UAV flight
duration, mobility pattern, limited energy source, and spectral efficiency.
Therefore, in [56], resource management was jointly optimized with the
UAV trajectory in wireless environment.
Limited amount of on-board energy is available for battery-operated UAV,
which must be used for propulsion and to fulfill communication-related
tasks [5]. Consequently, continuous and long-term wireless coverage
curtails the UAV flight time. In addition, UAV energy consumption also
depends on its path, weather condition, and mission of the UAV. Thus,
energy constraints of UAV must be explicitly taken into account during
planning of the UAV-based communication systems. Various works have
studied the interplay between energy efficiency and the optimal UAV
trajectory [53–55].
2.6 Conclusion
This chapter discussed the use of UAVs in wireless communication network,
specifically, the use of UAVs as aerial BSs and as aerial UE in cellular-assisted
systems. In both cases, the accurate channel model of the AG and AA prop-
agation is paramount, which must take into account the environmental
conditions, wireless channel impairments, and the UAV mobility to char-
acterize the performance of UAV-based communication network. Some
channel modeling efforts have been studied in this chapter. In addition,
key challenges, such as optimal deployment of UAVs, optimization of tra-
jectory path, resource management, and energy efficiency, have also been
highlighted.](https://image.slidesharecdn.com/18691482-250606172102-9f13893a/85/Autonomous-Airborne-Wireless-Networks-Ieee-Press-1st-Edition-Muhammad-Ali-Imran-Editor-46-320.jpg)
![27
3
Ultra-wideband Channel Measurements and Modeling
for Unmanned Aerial Vehicle-to-Wearables (UAV2W)
Systems
Amit Kachroo1
, Surbhi Vishwakarma1
, Jacob N. Dixon2
, Hisham
Abuella1
, Adithya Popuri1
, Qammer H. Abbasi3
, Charles F. Bunting1
,
Jamey D. Jacob4
, and Sabit Ekin1
1
School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA
2
IBM, Rochester, MN, USA
3
School of Engineering, University of Glasgow, Glasgow, UK
4School of Mechanical and Aerospace Engineering, Oklahoma State University, Stillwater, OK,
USA
3.1 Introduction
Over the past decades, wireless technology has seen an upward trend in the
bandwidth of the signals employed. The main reason behind this upward
trend is the proliferation of multimedia technologies that demand high data
rate, and also an increase in user base. Ultra-wideband (UWB) radio is one
of the creations of this trend where the bandwidth occupied by UWB tech-
nology is greater than or equal to 500 MHz. Therefore, UWB communication
technology exploits this large bandwidth to catch up with the high data rate.
Apart from the high bandwidth, the main advantages of UWB can be listed
as follows:
● Low power consumption with high data rate. The received power in UWB
lies very close to the noise floor [1–5].
● Control over duty cycle makes the battery last longer.
● Low probability of detection as it is close to the noise floor and any attempt
of jamming or eavesdropping will make the signal noisy [6].
● Small wavelength with low power makes it a perfect fit for body-centric
wireless network [3, 4].
Given these advantages, UWB is best suited for off-body communication.
Moreover, the Federal Communication Commission’s (FCC) guideline of the
Autonomous Airborne Wireless Networks, First Edition.
Edited by Muhammad Ali Imran, Oluwakayode Onireti, Shuja Ansari, and Qammer H. Abbasi.
© 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.](https://image.slidesharecdn.com/18691482-250606172102-9f13893a/85/Autonomous-Airborne-Wireless-Networks-Ieee-Press-1st-Edition-Muhammad-Ali-Imran-Editor-53-320.jpg)
![28 3 Ultra-wideband Channel Measurements and Modeling
power limit of −41.3 dBm or 75 nW/MHz identifies the UWB technology
as an unintentional interference source; the fact that it can thereby coex-
ist with other wireless technologies, especially at 2.4 GHz (WiFi, Bluetooth)
with minimal or no interference, reinforces the application of UWB tech-
nologies for off-body communication.
On the other hand, unmanned aerial vehicles (UAVs) are being now
used for remote healthcare deliveries especially to far flung areas that lack
connectivity. UAVs are also being used for emergency medical deliveries
where time is of utmost importance, such as during cardiac arrests [7–10].
One of the upcoming themes for UAVs is to directly monitor the health of
a patient by utilizing wearable patch devices [1, 7, 10–12]. The study in
this chapter explores the UWB technology with UAVs further for health
monitoring applications. This type of setup involving UAV and wearable
antenna/antennas is also known as unmanned aerial vehicle-to-wearable
(UAV2W) systems [1].
The closest one to this study is our previous work [1], where different
UWB bandwidths were considered for channel modeling in an indoor envi-
ronment. However, in this work, we consider the complete UWB bandwidth
of 7.5 GHz to study these body channels, and also to look into two different
environments and study the effect of postures. Also, previous studies such
as [5, 13–15] have performed on-body radio channel characterization and
modeling at 2.45 GHz but not at the UWB frequency. In addition [16–18] per-
formed off-body radio channel studies in a contained scenario with antennas
placed in standalone position. The other closest study is in [2, 3], where
off- and on-body channel characterizations are performed without the real
human subject. To the best of our knowledge, this is one of the first works
to consider UWB channel characterization between humans and UAV at
7.5 GHz bandwidth, and has studied different environments and the effects
of different body postures on the UWB system.
The rest of the chapter is organized as follows: Section 3.2 discusses the
measurement setup and data acquisition part, and Section 3.3 covers the
UWB-UAV2W radio channel characterization. Section 3.4 details the statis-
tical analysis and finally, Section 3.5 presents the conclusion based on the
measurement campaign done so far.
3.2 Measurement Settings
There are generally two methods to measure the channel response in a wire-
less communication, either time correlator based or frequency sweep based.
In our work, we have utilized the latter one by using a Vector Network](https://image.slidesharecdn.com/18691482-250606172102-9f13893a/85/Autonomous-Airborne-Wireless-Networks-Ieee-Press-1st-Edition-Muhammad-Ali-Imran-Editor-54-320.jpg)
Autonomous Airborne Wireless Networks Ieee Press 1st Edition Muhammad Ali Imran Editor
- 1. Autonomous Airborne Wireless Networks Ieee Press 1st Edition Muhammad Ali Imran Editor download https://ebookbell.com/product/autonomous-airborne-wireless- networks-ieee-press-1st-edition-muhammad-ali-imran- editor-37382964 Explore and download more ebooks at ebookbell.com
- 2. Here are some recommended products that we believe you will be interested in. You can click the link to download. Autonomous And Connected Heavy Vehicle Technology First Rajalakshmi Krishnamurthi https://ebookbell.com/product/autonomous-and-connected-heavy-vehicle- technology-first-rajalakshmi-krishnamurthi-44864412 Autonomous Navigation And Deployment Of Uavs For Communication Surveillance And Delivery Hailong Huang https://ebookbell.com/product/autonomous-navigation-and-deployment-of- uavs-for-communication-surveillance-and-delivery-hailong- huang-46079598 Autonomous Vehicle Technology Global Exploration And Chinese Practice Mingfang Du https://ebookbell.com/product/autonomous-vehicle-technology-global- exploration-and-chinese-practice-mingfang-du-46249306 Autonomous Vehicle Ethics The Trolley Problem And Beyond Ryan Jenkins https://ebookbell.com/product/autonomous-vehicle-ethics-the-trolley- problem-and-beyond-ryan-jenkins-46469512
- 3. Autonomous Trajectory Planning And Guidance Control For Launch Vehicles Zhengyu Song https://ebookbell.com/product/autonomous-trajectory-planning-and- guidance-control-for-launch-vehicles-zhengyu-song-48651146 Autonomous Vehicles For Public Transportation Clin Iclodean https://ebookbell.com/product/autonomous-vehicles-for-public- transportation-clin-iclodean-48680656 Autonomous Vehicles Volume 2 Smart Vehicles Romil Rawat Purvee Bhardwaj https://ebookbell.com/product/autonomous-vehicles-volume-2-smart- vehicles-romil-rawat-purvee-bhardwaj-48684352 Autonomous Vehicles Volume 1 Using Machine Intelligence 1st Edition A Mary Sowjanya https://ebookbell.com/product/autonomous-vehicles-volume-1-using- machine-intelligence-1st-edition-a-mary-sowjanya-48684364 Autonomous Nuclear Power Plants With Artificial Intelligence Jonghyun Kim https://ebookbell.com/product/autonomous-nuclear-power-plants-with- artificial-intelligence-jonghyun-kim-49159346
- 7. Autonomous Airborne Wireless Networks
- 9. Autonomous Airborne Wireless Networks Edited by Muhammad Ali Imran, Oluwakayode Onireti, Shuja Ansari, and Qammer H. Abbasi University of Glasgow, UK
- 10. This edition first published 2021 © 2021 John Wiley & Sons Ltd All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions. The right of Muhammad Ali Imran, Oluwakayode Onireti, Shuja Ansari, and Qammer H. Abbasi to be identified as the editors of this work has been asserted in accordance with law. Registered Offices John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK Editorial Office The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com. Wiley also publishes its books in a variety of electronic formats and by print-on-demand. Some content that appears in standard print versions of this book may not be available in other formats. Limit of Liability/Disclaimer of Warranty In view of ongoing research, equipment modifications, changes in governmental regulations, and the constant flow of information relating to the use of experimental reagents, equipment, and devices, the reader is urged to review and evaluate the information provided in the package insert or instructions for each chemical, piece of equipment, reagent, or device for, among other things, any changes in the instructions or indication of usage and for added warnings and precautions. While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Library of Congress Cataloging-in-Publication Data Applied for: ISBN: 9781119751687 Cover Design: Wiley Cover Images: © Shine Nucha/Shutterstock, © Solveig Been/Shutterstock Set in 9.5/12.5pt STIXTwoText by Straive, Chennai, India 10 9 8 7 6 5 4 3 2 1
- 11. v Contents Editor Biographies xiii List of Contributors xv 1 Introduction 1 Muhammad A. Imran, Oluwakayode Onireti, Shuja S. Ansari, and Qammer H. Abbasi 2 Channel Model for Airborne Networks 7 Aziz A. Khuwaja and Yunfei Chen 2.1 Introduction 7 2.2 UAV Classification 8 2.3 UAV-Enabled Wireless Communication 10 2.4 Channel Modeling in UAV Communications 11 2.4.1 Background 12 2.4.1.1 Path Loss and Large-Scale Fading 13 2.4.1.2 Small-Scale Fading 17 2.4.1.3 Airframe Shadowing 18 2.5 Key Research Challenges of UAV-Enabled Wireless Network 19 2.5.1 Optimal Deployment of UAVs 19 2.5.2 UAV Trajectory Optimization 19 2.5.3 Energy Efficiency and Resource Management 20 2.6 Conclusion 20 Bibliography 21
- 12. vi Contents 3 Ultra-wideband Channel Measurements and Modeling for Unmanned Aerial Vehicle-to-Wearables (UAV2W) Systems 27 Amit Kachroo, Surbhi Vishwakarma, Jacob N. Dixon, Hisham Abuella, Adithya Popuri, Qammer H. Abbasi, Charles F. Bunting, Jamey D. Jacob, and Sabit Ekin 3.1 Introduction 27 3.2 Measurement Settings 28 3.3 UWB-UAV2W Radio Channel Characterization 33 3.3.1 Path Loss Analysis 33 3.3.2 Time Dispersion Analysis 34 3.3.3 Path Loss Analysis for Different Postures 38 3.3.4 Time Dispersion Analysis for Different Postures 38 3.4 Statistical Analysis 42 3.5 Conclusion 44 Bibliography 44 4 A Cooperative Multiagent Approach for Optimal Drone Deployment Using Reinforcement Learning 47 Rigoberto Acosta-González, Paulo V. Klaine, Samuel Montejo-Sánchez, Richard D. Souza, Lei Zhang, and Muhammad A. Imran 4.1 Introduction 48 4.2 System Model 51 4.2.1 Urban Model 51 4.2.2 Communications Model 51 4.3 Reinforcement Learning Solution 54 4.3.1 Fully Cooperative Markov Games 54 4.3.2 Decentralized Q-Learning 57 4.3.3 Selection of Actions 58 4.3.4 Metrics 61 4.4 Representative Simulation Results 62 4.4.1 Simulation Scenarios 62 4.4.2 Environment 62 4.4.3 User Distribution 62 4.4.4 Simulation 63 4.4.5 Numerical Results 64 4.4.5.1 Single Frequency 64 4.4.5.2 Three Frequencies 65 4.4.5.3 Six Frequencies 66 4.5 Conclusions and Future Work 68 4.5.1 Conclusions 68
- 13. Contents vii 4.5.2 Future Work 69 Acknowledgments 69 Bibliography 69 5 SWIPT-PS Enabled Cache-Aided Self-Energized UAV for Cooperative Communication 73 Tharindu D. Ponnimbaduge Perera and Dushantha Nalin K. Jayakody 5.1 Introduction 73 5.2 System Model 77 5.2.1 Air-to-Ground Channel Model 80 5.2.2 Signal Structure 81 5.2.3 Caching Mechanism at the UAV 82 5.3 Optimization Problem Formulation 82 5.3.1 Maximization of the Achievable Information Rate at the User 82 5.3.2 Trajectory Optimization with Fixed Time and Energy Scheduling 84 5.4 Numerical Simulation Results 86 5.5 Conclusion 92 Acknowledgments 92 5.A Proof of Optimal Solutions Obtained in (P1) 93 Bibliography 94 6 Performance of mmWave UAV-Assisted 5G Hybrid Heterogeneous Networks 97 Muhammad K. Shehzad, Muhammad W. Akhtar, and Syed A. Hassan 6.1 The Significance of UAV Deployment 97 6.2 Contribution 98 6.3 The Potential of mmWave and THz Communication 98 6.4 Challenges and Applications 100 6.4.1 Challenges 101 6.4.1.1 Complex Hardware Design 101 6.4.1.2 Imperfection in Channel State Information 101 6.4.1.3 High Mobility 101 6.4.1.4 Beam Misalignment 101 6.4.2 Applications 102 6.5 Fronthaul Connectivity using UAVs 103 6.5.1 Distribution of SCBs 104 6.5.2 Placement of UAVs 104 6.6 Communication Model 105
- 14. viii Contents 6.6.1 Communication Constraints and Objective 107 6.7 Association of SCBs with UAVs 108 6.8 Results and Discussions 110 6.8.1 Analysis of Results 110 6.9 Conclusion 114 Bibliography 115 7 UAV-Enabled Cooperative Jamming for Physical Layer Security in Cognitive Radio Network 119 Phu X. Nguyen, Hieu V. Nguyen, Van-Dinh Nguyen, and Oh-Soon Shin 7.1 Introduction 119 7.2 System Model 121 7.2.1 Signal Model 121 7.2.2 Optimization Problem Formulation 125 7.3 Proposed Algorithm 125 7.3.1 Tractable Formulation for the Optimization Problem P2 126 7.3.1.1 Tractable Formulation for RS[n] 126 7.3.1.2 Tractable Formulation for RE[n] 127 7.3.1.3 Tractable Formulation for Constraint (7.10i) 127 7.3.1.4 Safe Optimization Problem 128 7.3.2 Proposed IA-Based Algorithm 128 7.4 Numerical Results 133 7.5 Conclusion 136 Bibliography 138 8 IRS-Assisted Localization for Airborne Mobile Networks 141 Olaoluwa Popoola, Shuja Ansari, Rafay I. Ansari, Lina Mohjazi, Syed A. Hassan, Nauman Aslam, Qammer H. Abbasi, and Muhammad A. Imran 8.1 Introduction 141 8.1.1 Related Work 143 8.1.2 Unmanned Aerial Vehicles 143 8.1.3 Intelligent Reflecting Surface 143 8.2 Intelligent Reflecting Surfaces in Airborne Networks 144 8.2.1 Aerial Networks with Integrated IRS 145 8.2.1.1 Integration of IRS in High-Altitude Platform Stations (HAPSs) for Remote Areas Support 145
- 15. Contents ix 8.2.1.2 Integration of IRS in UAVs for Terrestrial Networks Support 146 8.2.1.3 Integration of IRS with Tethered Balloons for Terrestrial/Aerial Users Support 147 8.2.2 IRS-Assisted Aerial Networks 147 8.3 Localization Using IRS 149 8.3.1 IRS Localization with Single Small Cell (SSC) 150 8.3.1.1 IRS Localization Using RSS with an SSC 150 8.4 Research Challenges 152 8.4.1 Challenges in UAV-Based Airborne Mobile Networks 152 8.4.2 Challenges in IRS-Based Localization 153 8.5 Summary and Conclusion 153 Bibliography 154 9 Performance Analysis of UAV-Enabled Disaster Recovery Networks 157 Rabeea Basir, Saad Qaisar, Mudassar Ali, Naveed Ahmad Chughtai, Muhammad Ali Imran, and Anas Hashmi 9.1 Introduction 157 9.2 UAV Networks 158 9.2.1 UAV System’s Architecture 159 9.2.1.1 Single UAV Systems 160 9.2.1.2 Multi-UAV Systems 161 9.2.1.3 Cooperative Multi-UAVs 161 9.2.1.4 Multilayer UAV Networks 162 9.3 Benefits of UAV Networks 163 9.4 Design Consideration of UAV Networks 166 9.5 New Technology and Infrastructure Trends 171 9.5.1 Network Function Virtualization (NFV) 179 9.5.2 Software-Defined Networks (SDNs) 179 9.5.3 Cloud Computing 180 9.5.4 Image Processing 180 9.5.5 Millimeter Wave Communication 181 9.5.6 Artificial Intelligence 182 9.5.7 Machine Learning 183 9.5.8 Optimization and Game Theory 184 9.6 Research Trends 184 9.7 Future Insights 187 9.8 Conclusion 188 Bibliography 188
- 16. x Contents 10 Network-Assisted Unmanned Aerial Vehicle Communication for Smart Monitoring of Lockdown 195 Navuday Sharma, Muhammad Awais, Haris Pervaiz, Hassan Malik, and Qiang Ni 10.1 Introduction 195 10.1.1 Relevant Literature 198 10.2 UAVs as Aerial Base Stations 199 10.2.1 Simulation Setting 200 10.2.2 Optimal Number of ABSs for Cellular Coverage in a Geographical Area 201 10.2.3 Performance Evaluation 202 10.2.3.1 Variation of Number of ABSs with ABS Altitude 202 10.2.3.2 Variation of Number of ABS with ABS Transmission Power 204 10.2.3.3 Variation of Number of ABSs with Geographical Area 205 10.3 UAV as Relays for Terrestrial Communication 207 10.3.1 5G Air Interface 209 10.3.2 Simulation Setup 210 10.4 Conclusion 212 Bibliography 213 11 Unmanned Aerial Vehicles for Agriculture: an Overview of IoT-Based Scenarios 217 Bacco Manlio, Barsocchi Paolo, Gotta Alberto, and Ruggeri Massimiliano 11.1 Introduction 217 11.2 The Perspective of Research Projects 218 11.3 IoT Scenarios in Agriculture 221 11.3.1 Use of Data and Data Ownership 224 11.4 Wireless Communication Protocols 224 11.5 Multi-access Edge Computing and 5G Networks 227 11.6 Conclusion 230 Bibliography 231 12 Airborne Systems and Underwater Monitoring 237 Elizabeth Basha, Jason To-Tran, Davis Young, Sean Thalken, and Christopher Uramoto 12.1 Introduction 237 12.2 Automated Image Labeling 239 12.2.1 Point Selection 239 12.2.2 Measurement System 239
- 17. Contents xi 12.2.3 Region Labeling 240 12.2.4 Testing 242 12.2.4.1 Measurement System Testing 242 12.2.4.2 Point Selection Simulations 243 12.2.4.3 Field Experiments 244 12.3 Water/Land Visual Differentiation 245 12.3.1 Classifier Training 245 12.3.2 Online Algorithm 246 12.3.3 Mapping 246 12.3.4 Transmit 247 12.3.5 Field Experiments 248 12.3.5.1 Calibration 248 12.3.5.2 Simulation 249 12.3.5.3 Overall 249 12.4 Offline Bathymetric Mapping 249 12.4.1 Algorithm Overview 250 12.4.2 Algorithm Simulation 250 12.4.3 Algorithm Implementation 251 12.4.4 Bathymetric Measurement System 252 12.5 Online Bathymetric Mapping 253 12.5.1 Point Selection Algorithms 254 12.5.1.1 Monotone Chain Hull Algorithm 254 12.5.1.2 Incremental Hull Algorithm 254 12.5.1.3 Quick Hull Algorithm 254 12.5.1.4 Gift Wrap Algorithm 255 12.5.1.5 Slope-Based Algorithm 255 12.5.1.6 Combination (Slope-Based and Probability) Algorithm 255 12.5.2 Simulation Setup 256 12.5.3 Results and Analysis 256 12.5.3.1 Spline 256 12.5.3.2 IDW 257 12.5.3.3 Overall Summary 258 12.6 Conclusion and Future Work 258 Bibliography 258 13 Demystifying Futuristic Satellite Networks: Requirements, Security Threats, and Issues 261 Muhammad Usman, Muhammad R. Asghar, Imran S. Ansari, and Marwa Qaraqe 13.1 Introduction 261 13.2 Inter-Satellite and Deep Space Network 262
- 18. xii Contents 13.2.1 Tier-1 of Satellite Networks 263 13.2.2 Tier-2 of Satellite Networks 264 13.2.3 Tier-3 of Satellite Networks 265 13.3 Security Requirements and Challenges in ISDSN 266 13.3.1 Security Challenges 267 13.3.1.1 Key Management 267 13.3.1.2 Secure Routing 268 13.3.2 Security Threats 269 13.3.2.1 Denial of Service Attack 269 13.3.2.2 Data Tampering 269 13.4 Conclusion 270 Bibliography 270 14 Conclusion 275 14.1 Future Hot Topics 275 14.1.1 Terahertz Communications 275 14.1.2 3D MIMO for Airborne Networks 276 14.1.3 Cache-Enabled Airborne Networks 276 14.1.4 Blockchain-Enabled Airborne Wireless Networks 276 14.2 Concluding Remarks 277 Index 279
- 19. xiii Editor Biographies Muhammad Ali Imran is Dean at the University of Glasgow, UESTC, Professor of Communication Systems, and Head of Communications Sensing and Imaging group in the James Watt School of Engineering at the University of Glasgow, UK. Oluwakayode Onireti is a lecturer at the James Watt School of Engineer- ing, University of Glasgow, UK. Shuja Ansari is currently a research associate at Communications Sensing and Imaging group in the James Watt School of Engineering at the Univer- sity of Glasgow, UK, and Wave-1 Urban 5G use case implementation lead at Glasgow 5G Testbed funded by the Scotland 5G Center. Qammer H. Abbasi is a senior lecturer (Associate Professor), Program Director for Dual PhD degree, and Deputy Head of Communications Sensing and Imaging group in the James Watt School of Engineering at the University of Glasgow, UK.
- 21. xv List of Contributors Qammer H. Abbasi James Watt School of Engineering University of Glasgow Glasgow UK Hisham Abuella School of Electrical and Computer Engineering, Oklahoma State University Stillwater, OK USA Rigoberto Acosta-González Department of Electronics and Telecommunications, Universidad Central “Marta Abreu” de Las Villas Santa Clara Cuba Muhammad W. Akhtar School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan Gotta Alberto Institute of Information Science and Technologies (ISTI) and Institute of Science and Technologies for Energy and Sustainable Mobility, National Research Council (CNR) Pisa Italy Mudassar Ali Department of Telecommunication Engineering, UET Taxila Pakistan Imran S. Ansari James Watt School of Engineering University of Glasgow Glasgow UK Rafay I. Ansari Department of Computer and Information Science Northumbria University Newcastle upon Tyne UK
- 22. xvi List of Contributors Shuja S. Ansari James Watt School of Engineering University of Glasgow Glasgow UK Muhammad R. Asghar School of Computer Science The University of Auckland Auckland New Zealand Muhammad Awais School of Computing and Communications Lancaster University Lancaster UK Elizabeth Basha Electrical and Computer Engineering Department University of the Pacific Stockton, CA USA Rabeea Basir School of Electrical Engineering & Computer Science (SEECS) National University of Sciences and Technology Islamabad Pakistan and James Watt School of Engineering University of Glasgow Glasgow UK Charles F. Bunting School of Electrical and Computer Engineering, Oklahoma State University Stillwater, OK USA Yunfei Chen School of Engineering University of Warwick Coventry UK Naveed A. Chughtai Military College of Signals National University of Sciences and Technology Rawalpindi Pakistan Jacob N. Dixon IBM Rochester, MN USA Sabit Ekin School of Electrical and Computer Engineering, Oklahoma State University Stillwater, OK USA Syed A. Hassan School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan
- 23. List of Contributors xvii Muhammad A. Imran James Watt School of Engineering University of Glasgow Glasgow UK Jamey D. Jacob School of Mechanical and Aerospace Engineering, Oklahoma State University Stillwater, OK USA Dushantha Nalin K. Jayakody Department of Information Technology, School of Computer Science and Robotics, National Research Tomsk Polytechnic University Tomsk Russian Federation and Centre for Telecommunication Research, School of Engineering Sri Lanka Technological Campus Padukka Sri Lanka Amit Kachroo School of Electrical and Computer Engineering, Oklahoma State University Stillwater, OK USA Aziz Khuwaja School of Engineering, Electrical and Electronic Engineering Stream University of Warwick Coventry UK Paulo V. Klaine Electronics and Nanoscale Engineering Department University of Glasgow Glasgow UK Hassan Malik Department of Computer Science Edge Hill University Ormskirk UK Bacco Manlio Institute of Information Science and Technologies (ISTI) and Institute of Science and Technologies for Energy and Sustainable Mobility, National Research Council (CNR) Pisa Italy Ruggeri Massimiliano National Research Council (CNR) Institute of Science and Technologies for Energy and Sustainable Mobility Ferrara Italy
- 24. xviii List of Contributors Lina Mohjazi James Watt School of Engineering University of Glasgow Glasgow UK Samuel Montejo-Sánchez Programa Institucional de Fomento a la I+D+i, Universidad Tecnológica Metropolitana Santiago Chile Hieu V. Nguyen The University of Danang – Advanced Institute of Science and Technology Da Nang Vietnam Qiang Ni School of Computing and Communications Lancaster University Lancaster UK Phu X. Nguyen Department of Computer Fundamentals, FPT University Ho Chi Minh City Vietnam Van-Dinh Nguyen Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg Luxembourg Oluwakayode Onireti James Watt School of Engineering University of Glasgow Glasgow UK and Department of Electrical Engineering, Sukkur IBA University Sukkur Pakistan Barsocchi Paolo Institute of Information Science and Technologies (ISTI) and Institute of Science and Technologies for Energy and Sustainable Mobility, National Research Council (CNR) Pisa Italy Haris Pervaiz School of Computing and Communications Lancaster University Lancaster UK Olaoluwa Popoola James Watt School of Engineering University of Glasgow Glasgow UK
- 25. List of Contributors xix Tharindu D. Ponnimbaduge Perera Department of Information Technology, School of Computer Science and Robotics, National Research Tomsk Polytechnic University Tomsk Russian Federation Adithya Popuri School of Electrical and Computer Engineering, Oklahoma State University Stillwater, OK USA Saad Qaisar School of Electrical Engineering & Computer Science (SEECS) National University of Sciences and Technology Islamabad Pakistan and Department of Electrical and Electronic Engineering University of Jeddah Jeddah Saudi Arabia Marwa Qaraqe Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University (HBKU) Doha Qatar Navuday Sharma Test Software Development Ericsson Eesti AS Tallinn Estonia Richard D. Souza Department of Electrical and Electronics Engineering, Federal University of Santa Catarina Florianóplis Brazil Muhammad K. Shehzad School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan Oh-Soon Shin School of Electronic Engineering Soongsil University Seoul South Korea Sean Thalken Electrical and Computer Engineering Department University of the Pacific Stockton, CA USA Jason To-Tran Electrical and Computer Engineering Department University of the Pacific Stockton, CA USA
- 26. xx List of Contributors Christopher Uramoto Electrical and Computer Engineering Department University of the Pacific Stockton, CA USA Muhammad Usman Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University (HBKU) Doha Qatar Surbhi Vishwakarma School of Electrical and Computer Engineering, Oklahoma State University Stillwater, OK USA Davis Young Electrical and Computer Engineering Department University of the Pacific Stockton, CA USA Lei Zhang Electronics and Nanoscale Engineering Department University of Glasgow Glasgow UK
- 27. 1 1 Introduction Muhammad A. Imran, Oluwakayode Onireti, Shuja S. Ansari, and Qammer H. Abbasi James Watt School of Engineering, University of Glasgow, Glasgow, UK Airborne networks (ANs) are now playing an increasingly crucial role in military, civilian, and public applications such as surveillance and monitoring, military, and rescue operations. More recently, airborne net- works have also become a topic of interest in the industrial and research community of wireless communication. The 3rd Generation Partnership Project (3GPP) standardization has a study item devoted to facilitating the seamless integration of airborne wireless networks into future cellular networks. Airborne wireless networks enabled by unmanned aerial vehicles (UAVs) can provide cost-effective and reliable wireless communications to support various use cases in future networks. Compared with high-altitude platforms or conventional terrestrial communications, the provision of on-demand communication systems with UAVs has faster deployment time and more flexibility in terms of reconfiguration. Further, UAV-enabled propagation can also offer better communication channels due to the existence of the line-of-sight (LoS) links, which are of short range. Despite the several benefits of airborne wireless networks, they suffer from some realistic constraints such as being energy constrained because of the limited battery power, safety concerns, and the strict flight zone. Hence, developing new signal processing, communication, and optimization framework for autonomous airborne wireless networks is essential. Such networks can offer high data rates and assist the traditional terrestrial networks to provide real-time and ultrareliable sensing applications for the beyond-5G networks. Achieving this gain requires the correct characterization of the propagation channel while considering the high Autonomous Airborne Wireless Networks, First Edition. Edited by Muhammad Ali Imran, Oluwakayode Onireti, Shuja Ansari, and Qammer H. Abbasi. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.
- 28. 2 1 Introduction mobility dynamics. Accurate channel modeling is imperative to fulfill the ever-increasing requirements of the end user to transfer data at higher rates. The air-to-ground (AG) and the air-to-air (AA) channel propagation models for the airborne wireless network channel can be characterized by using measurement and empirical studies. Further, the key performance indicators (KPIs) of airborne wireless networks such as flight time, trajec- tory, data rate, energy efficiency, and latency need to be optimized for the different use cases. This book explores recent advances in the theory and practice of airborne wireless networks for the next generation of wireless networks to support various applications, including emergency communications, coverage and capacity expansion, Internet of things (IoT), information dissemination, future healthcare, pop-up networks, etc. The book focuses on channel char- acteristics and modeling, networking architectures, self-organized airborne networks, self-organized backhaul, artificial-intelligence-enabled trajectory optimization, and application in sectors such as agriculture, underwater communications, and emergency networks. The book further highlights the main considerations during the design of the autonomous airborne networks and exploits new opportunities due to the recent advancement in wireless communication systems. This book for the first time evaluates the advances in the current state of the art and it provides readers with insights on how airborne wireless networks can seamlessly support various applications expected in future networks. More specifically, the book shows the readers how the integra- tion of self-organized networks and artificial intelligence can support the various use cases of airborne wireless networks. UAVs provide a suitable aerial platform for various wireless network appli- cations that require reliable and ubiquitous communication. The channel model plays a crucial role in the wireless communications system and thus Chapter 2 focuses on the channel model for UAV networks. The authors first provide an overview of UAV networks in terms of their classification and how they can be used to enable future wireless communication sys- tems. Accurate channel modeling is imperative to fulfill the ever-increasing requirements of the end user to transfer data at higher rates. Hence, the authors discuss channel modeling in UAV communications while focusing on the salient feature of the AG and AA propagation channels. Finally, the chapter concludes by discussing some of the key research challenges for the practical deployment of UAVs as airborne wireless nodes. In Chapter 3, the authors describe the fundamental properties of the ultrawide band (UWB) channel and present one of the first experimental off-body studies between a human subject and an UAV at 7.5 GHz of
- 29. 1 Introduction 3 bandwidth. In the study presented in this chapter, the transmitter antenna was placed on a UAV while the receiver antenna was patched on a human subject at different body locations during the campaign. The chapter presents the measurement setting, detailing the measurement campaign that was conducted in an indoor and an outdoor environment with LoS and non-line-of-sight (NLoS) cases. Furthermore, the chapter presents the UWB-unmanned aerial vehicle-to-wearables (UAV2W) channel characteri- zation. Finally, the chapter presents the statistical analysis to determine the distribution that best characterizes the fading channels between different body locations and the UAV. Chapter 4 describes the use of a Q-learning algorithm, which is based on a cooperative multiagent approach, to intelligently find the optimal posi- tion of a set of drones. The algorithm presented in the chapter is designed with the objective to minimize the number of users in an outage in the network. Hence, the algorithm determines the optimal distribution of fre- quencies and whether it should shut down a set of drones. The chapter also proposes and compares four different strategies for the Q-learning algorithm with different action selection policies, whose algorithms differ in terms of design complexity, ability to vary the number of drones in operation, and convergence time. The chapter presents numerical results that show the relationship between the density of users in the region of interest and the number of frequencies in operation. In Chapter 5, the authors describe a self-energized UAV-assisted caching relaying scheme. In this scheme, the UAV’s communication capabilities are powered solely by the power-splitting simultaneous wireless information and power transfer (PS-SWIPT) energy-harvesting (EH) technique, and it employs decode and forward (DF) relaying protocol to assist the informa- tion transmission to users from the source node. The authors present the transmission block diagram to accommodate communication processes within the system. Afterward, the authors address the problem of identi- fying optimal time and energy resources for the communication system and the optimal UAV’s trajectory while adhering to the quality of service (QoS) requirements of the communication network. Finally, numerical simulation results to identify the impacts of the system parameters on the information rate at the user equipment are presented. Chapter 6 focuses on the case study of millimeter-wave (mmWave) and terahertz (THz) communication and technical challenges for applying mmWave and THz frequency band for communication with UAVs. The chapter starts by presenting the potential of mmWave and THz bands for communications. This is followed by an overview of the technical chal- lenges for implementing mmWave and THz band for UAV communications.
- 30. 4 1 Introduction The chapter then presents a theoretical analysis that focuses on the place- ment of UAVs. Besides, the chapter investigates the performance of UAV-enabled hybrid heterogeneous network (HetNet) by considering strin- gent communication-related constraints such as the system bandwidth, data rate, signal-to-noise ratio (SNR), etc. The association of terrestrial small-cell base stations (SCBs) with UAVs is addressed such that the sum rate of the overall system is maximized. Finally, numerical results are included to show the favorable performance of the UAV-assisted wireless network. In Chapter 7, the authors discuss a method that uses a cooperative UAV as a friendly jammer to enhance the security performance of cognitive radio networks. The chapter starts by presenting the system model for the UAV-enabled cooperative jamming in a cognitive radio system. Then the optimization problem is formulated. The resource allocation in the network must jointly optimize the transmission power and UAV’s trajectory to maximize the secrecy rate while satisfying a given interference threshold at the primary receiver (PR). With the original problem non-convex, the authors first transform the original problem into a more tractable form and then present a successive convex approximation-based algorithm for its solutions. Finally, numerical results are included to show a significant improvement in the security performance of the UAV-enabled cognitive radio networks. Chapter 8 explores the possibility of using intelligent reflecting surfaces (IRS) in airborne networks for the localization of users and base stations. Positioning is an important aspect in the present and future wireless net- works, where it augments the network operations and assists in multiple localization-based applications. The chapter starts by presenting the related works and the underlying opportunities around IRS- and UAV-based base stations. The authors then discuss the integration of IRS in ANs and the potential use cases. Afterward, the chapter presents an IRS-based localiza- tion model for ANs along with some mathematical modeling. Finally, some future research challenges that present research opportunities are included. Chapter 9 describes the application of UAVs for disaster recovery net- works. The chapter starts by providing an overview of the UAV networks including the description of the UAV architectures, namely, single-UAV systems, multi-UAV systems, cooperative multi-UAV systems, and mul- tilayer UAV networks. The authors then discuss the most prominent applications of UAVs and the different system requirements of the UAV system. Afterward, the chapter discusses the design consideration of UAV networks in the context of disaster recovery networks. New technologies and infrastructure trends for UAV disaster networks namely, network function virtualization (NFV), software-defined networks (SDN), cloud
- 31. 1 Introduction 5 computing, and millimeter-wave networks are also discussed in the chapter. Further, the authors discuss the enhancement in technologies such as artificial intelligence, machine learning, optimization theory, and game theory as they impact the overall performance of the UAV-enabled disaster recovery networks. Finally, the chapter presents the research trends and some insight into the future. In Chapter 10, the authors discuss the importance of UAVs in monitoring COVID-19 restrictions of social distancing, public gatherings, and physical contacts in a smart city environment. The chapter starts with a review of recent literature addressing the impact of COVID-19 in the current scenario and strategies to find potential solutions with existing communication and computing technologies. Afterward, the authors present two use case sce- narios of UAVs namely, UAVs as aerial base stations (ABS) and UAVs as Relays, while including the simulation setups with ray tracing for both sce- narios. The chapter then presents the derivation of the optimal number of ABSs to cover a geographical region, given the constraint on ABS transmis- sion power, the altitude of hovering, and including the path loss and channel fading effects from ray-tracing simulations. The authors then describe the 5G air interface when using the UAVs as relays. Finally, simulation results on the received power by the ground users and the throughput coverage area are presented. In Chapter 11, the authors present and discuss both the research initia- tives and the scientific literature on IoT-based smart farming (SF), especially the use of UAVs in SF. The authors start by presenting an analysis of how UAVs are used in SF and the application scenarios. This is then followed by a detailed review of the scientific work in the literature highlighting the role of unmanned vehicles. The chapter then presents both the requirements and solutions for networking and a brief comparison of the existing pro- tocol supporting IoT scenarios in agricultural settings. Finally, the chapter discusses the potential future role of the joint use of mobile edge comput- ing (MEC) and the 5G network, presenting network architecture to connect smart farms through UAVs and satellites. Wetlands monitoring requires accurate topographic and bathymetric maps, and this can be achieved using UAVs that can create maps regularly, with minimum cost and reduced environmental impact. Chapter 12 introduces a set of systems needed to create this automation. The chapter starts by discussing the automated image labeling system. Next, the authors present an online classification system for differentiating land and water. The authors then present offline bathymetric map creation using aerial robots. Since the offline approach does not take full advantage of the adapt- ability that the UAV provides, the authors present the online bathymetric
- 32. 6 1 Introduction mapping. Finally, the chapter presents results and analysis to show the best combination of the online bathymetric mapping. Integration of terrestrial and satellite networks has been proposed for leveraging the combined benefits of both complementary technologies. Moreover, with the quest of exploring deep space and connecting solar system planets with the Earth, the traditional satellite network has gone beyond the geosynchronous equatorial orbit (GEO) wherein Interplanetary Internet will play a key role. Chapter 13 presents a short review of the inter-satellite and deep space network (ISDSN). This chapter discusses the classification of the ISDSN into different tiers while highlighting the communication and networking paradigms. Further, the chapter also dis- cusses the security requirements, challenges, and threats in each tier. The potential solutions to the identified challenges at the different tiers of the ISDSN are also described. Finally, the chapter concludes by highlighting the crucial role of the ISDSN in future cellular networks.
- 33. 7 2 Channel Model for Airborne Networks Aziz A. Khuwaja1,2 and Yunfei Chen1 1 School of Engineering, Electrical and Electronic Engineering Stream, University of Warwick, Coventry, UK 2Department of Electrical Engineering, Sukkur IBA University, Sukkur, Pakistan 2.1 Introduction The use of unmanned aerial vehicles (UAVs) is desirable due to their high maneuverability, ease of operability, and affordable prices in various civilian applications, such as disaster relief, aerial photography, remote surveillance, and continuous telemetry. One of the promising application of UAVs is enabling the wireless communication network in cases of natural calamity and in hot spot areas during peak demand where the resources of the existing communication network have been depleted [1]. Qualcomm has already initiated field trials for the execution of fifth generation (5G) cellular applications [2]. Google and Facebook are also exploiting the use of UAVs to provide Internet access to far-flung destinations [3]. The selection of an appropriate type of UAV is essential to meet the desired quality of service (QoS) depending on applications and goals in different environments. In fact, for any specific wireless networking application, the UAV altitude and its capabilities must be taken into account. UAVs can be categorized, based on their altitude, into low-altitude platforms (LAPs) and high-altitude platforms (HAPs). Furthermore, based on their structure, UAVs can be categorized as fixed-wing and rotary-wing UAVs. In compar- ison with rotary wings, fixed-wing UAVs move in the forward direction to remain aloft, whereas rotary-wing UAVs are desired for applications that require UAVs to be quasi-stationary over a given area. However, in both types, flight duration depends on their energy sources, weight, speed, and trajectory. Autonomous Airborne Wireless Networks, First Edition. Edited by Muhammad Ali Imran, Oluwakayode Onireti, Shuja Ansari, and Qammer H. Abbasi. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.
- 34. 8 2 Channel Model for Airborne Networks The salient features of UAV-based communication network are the air-to-ground (AG) and air-to-air (AA) propagation channels. Accurate channel modeling is imperative to fulfill the ever-increasing requirements of end users to transfer data at higher rates. The available channel models for AG propagation are designed either for terrestrial communication or for aeronautical communications at higher altitudes. These models are not preferable for low-altitude UAV communication, which uses small size UAVs in different urban environments. On one hand, the AG channel exhibits higher probability of line-of-sight (LoS) propagation, which reduces the transmit power requirement and provides higher link reliability. In cases with non-line-of-sight (NLoS), shadowing and diffraction losses can be compensated with a large elevation angle between the UAV and the ground device. On the other hand, UAV mobility can incur significant temporal variations in both the AG and AA propagation due to the Doppler shift. Small UAVs may experience airframe shadowing due to their flight path with sharper changes in pitch, yaw, and roll angle. In addition, distinct struc- tural design and material of UAV body may contribute additional shadowing attenuation. This phenomenon has not yet been extensively studied in the literature. Despite the number of promising UAV applications, one must address sev- eral technical challenges before the widespread applicability of UAVs. For example, while using UAV in aerial base station (BS) scenario, the impor- tant design considerations include radio resource management, flight time, optimal three-dimensional deployment of UAV, trajectory optimization, and performance analysis. Meanwhile, considering UAV in the aerial user equipment (UE) scenario, the main challenges include interference man- agement, handover management, latency control, and three-dimensional localization. However, in both scenarios, channel modeling is an important design step in the implementation of UAV-based communication network. This chapter provides an overview of the use of UAV as aerial UEs and aerial BSs and discusses the technical challenges related to AG channel modeling, airframe shadowing, optimal deployment of UAVs, trajectory optimization, resource management, and energy efficiency. 2.2 UAV Classification The need for an appropriate type of UAV depends on the specific mission, environmental conditions, and civil aviation regulations to attain certain
- 35. 2.2 UAV Classification 9 Table 2.1 Regulation for LAP deployment of UAVs in different countries. Country Maximum altitude (m) Minimum distance to humans (m) Minimum distance to airport (km) US 122 — 8 UK 122 50 — Chile 130 36 — Australia 120 30 5.5 South Africa 46 50 10 altitude. In addition, for any particular UAV-enabled wireless networking application, several factors, such as the number of UAVs, their optimal deployment, and QoS requirement, must be taken in to account. The operational altitude of the UAV from the ground level can be categorized as LAP and HAP. UAVs in LAP can fly between the altitude ranges from tens of meters to a few kilometers [4]. However, civil aviation authorities of some countries have set the operational altitude of UAVs up to a few hundred meters to avoid airborne collision with commercial flights. For example, Table 2.1 lists the regulations of maximum allowable LAP deployment of UAVs in various countries without any specific permit [5]. HAPs, on the other hand, have altitudes above 17 km where UAVs are typically quasi-stationary [1, 4]. For time-sensitive applications such as emergency services, LAPs are more appropriate then HAPs due to their rapid deployment, quick mobility, and cost-effectiveness. Furthermore, LAPs can be used for collecting sensor data from the ground. In this case, LAPs can be readily replaced or recharged as needed. In contrast, HAPs are preferred due to their long endurance (days or months) operations and wider ground coverage [1]. However, operational cost of HAPs is high and their deployment time is significantly longer. UAV can also be categorized based on their structure into rotary-wing and fixed-wing UAVs. Rotary-wing UAVs are powered by rotating blades, and based on the number of blades they are termed as either quadcopter with four blades, hexacopter with six blades, or octocopter with eight blades. On the other hand, fixed-wing UAVs include those that are driven by propellers with small size engine and have wings that are fixed. However, the flight time of UAVs relies on several key factors, such as type, weight, speed, energy sources (battery or engine), and trajectory of the UAV.
- 36. 10 2 Channel Model for Airborne Networks Aerial user equipment Aerial base station Figure 2.1 Aerial user equipment and aerial base station. 2.3 UAV-Enabled Wireless Communication UAVs can operate as aerial UE as shown in Figure 2.1. For example, aerial surveillance can be a cost-effective solution to provide access to those ter- rains that may be difficult to reach by humans in land vehicles. In this case, UAVs equipped with camera and sensors are used to gather video recordings and live images of a specific target on the ground and data from the sensor. Thereafter, the UAV has to coordinate with the ground user via existing cel- lular infrastructure and transfer the collected information with certain reli- ability, throughput, and delay while achieving the QoS requirements. The first scenario in Figure 2.1 (left side) requires a better connectivity between the aerial UE and at least one of the BSs installed typically at the ground. However, a performance drop is expected in the presence of aerial BSs acting as interferers. Moreover, the coexistence between the aerial UE, terrestrial UE, and the cellular infrastructure has to be studied. On the other hand, UAVs provide power efficiency and mobility to deploy as aerial BS in the future wireless networks. In this case, the mobility of UAV can dynamically provide additional on-demand capacity. This advantage of UAV-enabled network can be exploited by service providers for densification of network, temporary coverage of an area, or quick network deployment in an emergency scenario. Moreover, localization service precision can be improved due to the favorable propagation conditions between the UAV and the ground user. The second scenario in Figure 2.1 (right side) requires a better link between one of the multiple aerial BSs and all the terrestrial UEs. In comparison with fixed BSs, the aerial BSs are capable of adjusting their
- 37. 2.4 Channel Modeling in UAV Communications 11 altitude to provide good LoS propagation. However, the key challenge in this scenario is the optimum placement of aerial BSs to maximize the ground coverage for higher achievable throughput. 2.4 Channel Modeling in UAV Communications In wireless communications, the propagation channel is the free space between the transmitter and the receiver. It is obvious that the performance of wireless networks is influenced by the characteristics of the propaga- tion channel. Therefore, knowledge of wireless channels is pertinent in designing UAV-enabled networks for future wireless communication. Fur- thermore, the characterization of radio channel and its modeling for UAV network architecture are crucial for the analysis of network performance. Majority of the channel modeling efforts is devoted to the terrestrial radio channel with fixed infrastructure. However, these channel models may not be completely suitable for wireless communication using UAVs because of their mobility and small size. The AG channel between the UAV and the ground user implies higher link reliability and requires lower transmission power due to the higher probability of LoS propagation. In the case of NLoS, power variations are more severe because the ground-based side of the AG link is surrounded by obstacles that adversely affect the propagation. Figure 2.2 depicts the AG propagation channel and shows the distinction between LoS and NLoS components of the channel, with dp being the propagation distance. Furthermore, temporal variations and the Doppler shift are caused by the UAV mobility. As a result, the arbitrary UAV mobility pattern and operational environment are challenges in modeling the AG Figure 2.2 Air-to-ground propagation in UAV-assisted cellular network. h θ LoS NLoS d hG dp
- 38. 12 2 Channel Model for Airborne Networks channel. Apart from the AG propagation channel, other factors such as airframe shadowing and on-board antenna placement and characteristics can influence the received power strength. In addition, AA channels between airborne UAVs mostly experience strong LoS similar to the high-altitude AG channels. However, Doppler shift is higher because UAV mobility is significantly higher and it is difficult to maintain alignment between multiple UAVs. Accurate AG and AA propagation channel models are imperative for the optimal deployment and the design of the UAV communication networks. This section will discuss recent efforts in the modeling of AG and AA prop- agation channels. 2.4.1 Background In wireless communications, several propagation phenomena occur when electromagnetic waves radiate from the transmitter in several directions and interact with the surrounding environment before reaching the receiver. As shown in Figure 2.3, propagation phenomena such as reflection, scattering, diffraction, and penetration occur due to the natural obstacles and build- ings, which provoke the multiple realization of the signal transmitted from the UAV, often known as multipath components (MPC). Thus, each compo- nent received at the receiver with different amplitude, phase, and delay, and the resultant signal is a superposition of multiple copies of the transmitted signal, which can interfere either constructively or destructively depending Diffracted path S c a t t e r e d p a t h R e fl e c t e d p a t h Lo S pa th Figure 2.3 Multipath air-to-ground propagation in urban setting.
- 39. 2.4 Channel Modeling in UAV Communications 13 on their respective random phases [6]. Typically, several fading mechanisms are added linearly in dB to represent the radio channel as y = PL + XL + XS, (2.1) where PL is the distance-dependent path loss, XL is the large-scale fading consisting of power variation on a large scale due to the environment, and XS is the small-scale fading. Parameters of channel model, such as path loss exponent and LoS probability, are dependent on the altitude level because propagation conditions change at different altitudes. The airspace is often segregated into three propagation echelons or slices as follows: ● Terrestrial channel: For suburban and urban environments, altitude is between 10 and 22.5 m, respectively [7]. In this case, the terrestrial channel models can be used to model AG propagation because the air- borne UAV is below the rooftop level. As a result, NLoS is the dominant component in the propagation. ● Obstructed AG channel: For suburban and urban environments, altitude is 10–40 m and 22.5–100 m, respectively. In this case, LoS probability is higher than that of the terrestrial channels. ● High-altitude AG channel: All channels are in LoS for the altitude ranges between 100 and 300 m or above. Consequently, the propagation is similar to that in the free space case. Moreover, no shadowing is experienced for these channels. 2.4.1.1 Path Loss and Large-Scale Fading Air-to-Air Channel Free space path loss model is the simplest channel model to represent the AA propagation at a relatively high altitude. Thus, the received power is given by [6] PR = PTGTGR ( 𝜆c 4𝜋d )2 , (2.2) where PT denotes the transmitted signal power, GT and GR represent the gain of the transmitter and receiver antennas, respectively, d is the ground dis- tance between the transmitter and receiver, and 𝜆c is the carrier wavelength. Path loss exponent (𝜂) is the rate of distance-dependent power loss, where 𝜂 varies with environments. In Eq. (2.2), 𝜂 = 2 for free space propagation. Therefore, the distance-dependent path loss expression can be generalized as PL = ( 4𝜋d 𝜆c )𝜂 . (2.3)
- 40. 14 2 Channel Model for Airborne Networks Air-to-Ground Channel In urban environment, the AG channel may not expe- rience complete free space propagation. In the existing literature on UAV communications, the log-distance model is the prominently used path loss model due to its simplicity and applicability when environmental parame- ters are difficult to define. Therefore, path loss in dB is given by PL(d) = PL0 + 10𝜂 log ( d d0 ) , (2.4) where PL0 = 20 log ( 4𝜋d0 𝜆c ) is the path loss for the reference distance d0. For the same propagation distance between the ground device and the UAV, large-scale variations are different at different locations within the same environment because the materials of obstacles vary from each other, which affects the radio signal propagation. As a result, at any distance d, XL in Eq. (2.1) is the shadow fading measured in dB and modeled as the normal random variable with variance 𝜎 in dB. This model is extensively applied for modeling of the terrestrial channels. Table 2.2 lists some measurement campaigns for the estimations of path loss and large-scale effects. Another popular channel model to characterize the AG propagation in UAV communications is the probabilistic path loss model in [4] and [17]. In [17], the path loss between the ground device and the UAV is dependent on the position of the UAV and the propagation environments (e.g. suburban, urban, dense-urban, high-rise). Consequently, during the AG radio propa- gation, the communication link can be either LoS or NLoS depending on the environment. Many of the existing works [18–35] on UAV communi- cations adopted the probabilistic path loss model of [4] and [17]. In these works, the probability of occurrence of LoS and NLoS links are functions of the environmental parameters, height of the buildings, and the elevation angle between the ground device and the UAV. This model is based on envi- ronmental parameters defined in the recommendations of the International Telecommunication Union (ITU). In particular, ITU-R provides statistical parameters related to the environment that determine the height, number, and density of the buildings or obstacles. For instance, in [36], the height of the buildings can be modeled by using the Rayleigh distribution. The aver- age path loss for the AG propagation in [17] is given as PL = ℙLoS × PLLoS + ( 1 − ℙLoS ) × PLNLoS, (2.5) where PLLoS and PLNLoS are the LoS and NLoS path loss, respectively, for the free space propagation. ℙLoS is the LoS probability given as ℙLoS = 1 1 + e−(𝜃−) , (2.6)
- 41. Table 2.2 Measurement campaigns to characterize the path loss and large-scale AG propagation fading. References Scenario 𝜼 PL0 (dB) 𝝈 (dB) Yanmaz et al. [8] Urban/Open field 2.2–2.6 — — Yanmaz et al. [9] Open field 2.01 — — Ahmed et al. [10] — 2.32 — — Khawaja et al. [11] Suburban/Open field 2.54–3.037 21.9–34.9 2.79–5.3 Newhall et al. [12] Urban/Rural 4.1 — 5.24 Tu and Shimamoto [13] Near airports 2–2.25 — — Matolak and Sun [14] Suburban 1.7 (L-band) 98.2–99.4 (L-band) 2.6–3.1 (L-band) 1.5–2 (C-band) 110.4–116.7 (C-band) 2.9–3.2 (C-band) Sun and Matolak [15] Mountains 1–1.8 96.1–123.9 2.2–3.9 Meng and Lee [16] Over sea 1.4–2.46 19–129 —
- 42. 16 2 Channel Model for Airborne Networks where and are the constant values related to the environment, 𝜃 = arctan ( h d ) is the elevation angle between the ground user and the UAV, h is the altitude of the UAV, and d is the distance between the ground projection of the UAV and the ground device. According to Eq. (2.6), as the elevation angle increases with the UAV altitude, the blockage effect decreases and the AG propagation becomes more LoS. An advantage of this model is that it is applicable for different environments and for different UAV altitudes. However, it is unable to capture the impact of path loss for AG propagation in mountainous regions and over water bodies due to the lack of information related to their statistical parameters. Conventional well-known channel models for cellular communications can be used for UAV communications for UAV altitude between 1.5 and 10 m. One such model for the macro-cell network was designed for the rural environment by the 3rd Generation Partnership Project (3GPP) in [7, 37]. Since LoS and NLoS links are treated separately, the probability of LoS propagation is expressed as ℙG LoS = { 1, if d ≤ 10 m, e− d−10 1000 , if 10 m < d. (2.7) Path loss and large-scale fading can be calculated once the LoS probability is known from Eq. (2.7). As the communication nodes change their position, path loss also changes and can be found as PLG LoS = { PLG 1 , if 10 m ≤ d ≤ ̂ d, PLG 2 , if ̂ d ≤ d ≤ 10 km, (2.8) PLG NLoS = max ( PLG LoS, ̂ PL G NLoS ) , for 10 m ≤ d ≤ 5 km, (2.9) where PLG 1 = 20 log ( 40𝜋dfc 3 ) + min (0.03h1.72 , 10) log(d) − min (0.44h1.72, 14.77) + 0.002d log(h), (2.10) PLG 2 = PLG 1 + 40 log ( d ̂ d ) , (2.11) ̂ PL G NLoS =161.04 − 7.1 log(𝑤) + 7.5 log(h) − ( 24.37 − 3.7 ( h hG )2 ) log(hG) + ( 43.42 − 3.1 log ( hG )) ( log(d) − 3 ) + 20 log( fc) − ( 3.2 log (11.75h)2 − 4.97 ) , (2.12) ̂ d = 2𝜋hhG fc c , (2.13)
- 43. 2.4 Channel Modeling in UAV Communications 17 with fc, hG, 𝑤, and c being the carrier frequency, height of ground BS, the average width of street, and the speed of light, respectively. For the obstructed AG propagation with the UAV altitude between 10 and 40 m, the LoS probability in the rural environment for the macro-cell net- work can be computed as [7] ℙA LoS = ⎧ ⎪ ⎨ ⎪ ⎩ 1, if d ≤ ̃ d, ̃ d d + e ( −d p1 )( 1− −̃ d d ) , if ̃ d < d, (2.14) where ̃ d = max ( 1350.8 log(h) − 1602, 18 ) , (2.15) p1 = max ( 15021 log(h) − 160 53, 1000 ) . (2.16) The path loss for LoS and NLoS links can be computed as PLA LoS = max ( 23.9 − 1.8 log(h), 20 ) log(d) + 20 log ( 40𝜋fc 3 ) , (2.17) PLA NLoS = max ( PLA LoS, −12 + (35 − 5.3 log(h)) log(d) + 20 log ( 40𝜋fc 3 )) . (2.18) For a high-altitude AG channel with 40 m < h ≤ 300 m, the LoS probabil- ity is 1 and the path loss can be formulated as Eq. (2.17). 2.4.1.2 Small-Scale Fading Small-scale fading refers to the random fluctuations of amplitude and phase of the received signal over a short distance or a short period of time due to constructive or destructive interference of the MPC. For different propaga- tion environments and wireless systems, different distribution models are suggested to analyze the random variations in the received signal envelop. The Rician and Rayleigh distributions are widely used models in the literature of wireless communications, where both are based on the central limit theorem. The Rician distribution provides better fit for the AA and AG channels, where the impact of LoS propagation is stronger. On the other hand, when the MPC impinges at the receiver with random amplitude and phase, the small-scale fading effect can be captured by the Rayleigh distribution [6]. Geometrical analysis, numerical simulations, and empirical data are used to obtain the stochastic fading models [38–40]. Geometry-based stochastic
- 44. 18 2 Channel Model for Airborne Networks Table 2.3 Measured small-scale fading of AG propagation in different environments. References Scenario Frequency band Fading distribution Khawaja et al. [11] Suburban/Open field Ultra-wideband Nakagami Newhall et al. [12] Urban/Suburban Wideband Rayleigh, Rician Tu and Shimamoto [13] Urban/Suburban Wideband Rician Matolak and Sun [14] Urban/Suburban Wideband Rician Simunek et al. [45] Urban/Suburban Narrowband Rician Cid et al. [46] Forest/Foliage Ultra-wideband Rician, Nakagami Matolak and Sun [47] Sea/Fresh water Wideband Rician channel model (GBSCM) is the most popular type of small-scale fading model. GBSCM is subdivided into regular-shaped geometry-based stochas- tic channel model (RS-GBSCM) and irregular-shaped geometry-based stochastic channel model (IS-GBSCM). Time-variant IS-GBSCM was pre- sented in [41] and RS-GBSCM was presented in [42] and [43].These works illustrated Rician distribution for small-scale fading. In [44], non-geometric stochastic channel model (NGSCM) was provided, where small-scale effects of AG propagation were modeled by using Rician and Loo models. Table 2.3 provides the measured characteristics of small-scale fading of AG propagation in different environments. 2.4.1.3 Airframe Shadowing Airframe shadowing occurs when the LoS of AG propagation is obstructed by the UAV structure. This impairment is unique to UAV communications for both AA and AG channels and does not exist in conventional cellular communications. Airframe shadowing is more severe in fixed-wing UAVs mounted with single antenna. In this case, the AG communication link can be severe during roll, pitch, or yaw motion of the UAV. One possible solution to alleviate airframe shadowing is to replace the single-antenna system with spatially separated multiple antennas. Other factors responsible for airframe shadowing are the size, shape, and material of the UAV. The seminal work on the measurement of airframe shadowing was performed
- 45. 2.5 Key Research Challenges of UAV-Enabled Wireless Network 19 in [48], which found that the aircraft roll angle was proportional to the shad- owing attenuation. Moreover, shadowing duration depends on the flight maneuvering. 2.5 Key Research Challenges of UAV-Enabled Wireless Network This section discusses some of the key research challenges for the practical deployment of UAVs as airborne wireless nodes. 2.5.1 Optimal Deployment of UAVs In UAV-based communications, one of the key challenges is the optimal three-dimensional deployment of hovering UAV. The capability of UAV to maneuver and adjust its altitude provides additional degree of freedom for UAV deployment in an efficient manner to improve capacity and coverage. In fact, UAV deployment is more challenging in UAV communications than in conventional terrestrial communications because the characteristics of AG propagation change with the position of the UAV. However, for efficient UAV deployment, flight duration and energy constraints must be taken into account for battery-operated UAV, as they affect the performance of networks. In addition, simultaneous deployment of multiple UAVs is more challenging because of the co-channel interference and the possibility of airborne collision of UAVs. Another important issue is the UAV deploy- ment in the presence of terrestrial network. UAV deployment problem has been extensively discussed in the literature for coverage maximization [17, 29, 30, 33, 33], data collection from Internet of Things (IoT) devices [31], UAV-assisted wireless network [27], disaster scenario [49], and caching applications [22]. 2.5.2 UAV Trajectory Optimization Optimal trajectory design for mobile UAV is an important issue in UAV-based communications. Specifically, optimal path planning is crucial for UAVs operating for data collection from ground-based sensors and caching scenarios. UAV trajectory planning is mostly effected by the dimen- sion of the target area, flight duration of the mission, QoS requirement by the ground users, and energy constraints. Apart from physical parameters, UAV trajectory optimization is analytically a challenging problem because it involves a fixed number of optimization variables related to the UAV
- 46. 20 2 Channel Model for Airborne Networks locations [1]. In addition, UAV trajectory optimization requires coupling between different QoS metrics in wireless communication with the mobil- ity of UAV. Recently, there have been a number of studies on the joint trajectory optimization of UAV with its wireless communication metrics, such as throughput maximization in [50–52] and energy-efficient UAV communication in [53, 54]. 2.5.3 Energy Efficiency and Resource Management Energy efficiency and resource management require attention where UAVs are operating in key scenarios to collect data from IoT devices, ensure pub- lic safety, and support cellular wireless network. Resource management is a major challenge in UAV communications unlike in cellular communica- tions [55]. However, UAV communications introduce additional hindrance in radio resource management due to the interplay between the UAV flight duration, mobility pattern, limited energy source, and spectral efficiency. Therefore, in [56], resource management was jointly optimized with the UAV trajectory in wireless environment. Limited amount of on-board energy is available for battery-operated UAV, which must be used for propulsion and to fulfill communication-related tasks [5]. Consequently, continuous and long-term wireless coverage curtails the UAV flight time. In addition, UAV energy consumption also depends on its path, weather condition, and mission of the UAV. Thus, energy constraints of UAV must be explicitly taken into account during planning of the UAV-based communication systems. Various works have studied the interplay between energy efficiency and the optimal UAV trajectory [53–55]. 2.6 Conclusion This chapter discussed the use of UAVs in wireless communication network, specifically, the use of UAVs as aerial BSs and as aerial UE in cellular-assisted systems. In both cases, the accurate channel model of the AG and AA prop- agation is paramount, which must take into account the environmental conditions, wireless channel impairments, and the UAV mobility to char- acterize the performance of UAV-based communication network. Some channel modeling efforts have been studied in this chapter. In addition, key challenges, such as optimal deployment of UAVs, optimization of tra- jectory path, resource management, and energy efficiency, have also been highlighted.
- 47. Bibliography 21 Bibliography 1 Zeng, Y., Zhang, R., and Lim, T.J. (2016). Wireless communications with unmanned aerial vehicles: opportunities and challenges. IEEE Communi- cations Magazine 54 (5): 36–42. 2 Qualcomm Technologies Inc. (2016). Leading the World to 5G: Evolv- ing Cellular Technologies for Safer Drone Operation. Technical report. Qualcomm. 3 Patterson, T. (2015). Google, Facebook, SpaceX, OneWeb plan to beam internet everywhere. https://edition.cnn.com/2015/10/30/tech/pioneers- google-facebook-spacex-oneweb-satellite-drone-balloon-internet/index .html (accessed 08 March 2021). 4 Al-Hourani, A., Kandeepan, S., and Jamalipour, A. (2014). Modeling air-to-ground path loss for low altitude platforms in urban environments. 2014 IEEE Global Communications Conference, pp. 2898–2904. 5 Fotouhi, A., Qiang, H., Ding, M. et al. (2019). Survey on UAV cellular communications: practical aspects, standardization advancements, regu- lation, and security challenges. IEEE Communications Surveys Tutorials 21 (4): 3417–3442. 6 Molisch, A. (2011). Wireless Communications. Wiley - IEEE. 7 3GPP (2017). Study on Enhanced LTE Support for Aerial Vehicles. Tech- nical report, 3rd Generation Partnership Project 3GPP. 8 Yanmaz, E., Kuschnig, R., and Bettstetter, C. (2011). Channel measure- ments over 802.11a-based UAV-to-ground links. 2011 IEEE GLOBECOM Workshops (GC Wkshps), pp. 1280–1284. 9 Yanmaz, E., Kuschnig, R., and Bettstetter, C. (2013). Achieving air-ground communications in 802.11 networks with three-dimensional aerial mobility. 2013 Proceedings IEEE INFOCOM, pp. 120–124. 10 Ahmed, N., Kanhere, S.S., and Jha, S. (2016). On the importance of link characterization for aerial wireless sensor networks. IEEE Communica- tions Magazine 54 (5): 52–57. 11 Khawaja, W., Guvenc, I., and Matolak, D. (2016). UWB channel sound- ing and modeling for UAV air-to-ground propagation channels. 2016 IEEE Global Communications Conference (GLOBECOM), pp. 1–7. 12 Newhall, W.G., Mostafa, R., Dietrich, C. et al. (2003). Wideband air-to-ground radio channel measurements using an antenna array at 2 GHz for low-altitude operations. IEEE Military Communications Conference, 2003. MILCOM 2003, Volume 2, pp. 1422–1427. 13 Tu, H.D. and Shimamoto, S. (2009). A proposal of wide-band air-to-ground communication at airports employing 5-GHz band. 2009 IEEE Wireless Communications and Networking Conference, pp. 1–6.
- 48. 22 2 Channel Model for Airborne Networks 14 Matolak, D.W. and Sun, R. (2017). Air-ground channel character- ization for unmanned aircraft systems-part III: the suburban and near-urban environments. IEEE Transactions on Vehicular Technology 66 (8): 6607–6618. 15 Sun, R. and Matolak, D.W. (2017). Air-ground channel characterization for unmanned aircraft systems part II: Hilly and mountainous settings. IEEE Transactions on Vehicular Technology 66 (3): 1913–1925. 16 Meng, Y.S. and Lee, Y.H. (2011). Measurements and characterizations of air-to-ground channel over sea surface at C-band with low airborne altitudes. IEEE Transactions on Vehicular Technology 60 (4): 1943–1948. 17 Al-Hourani, A., Kandeepan, S., and Lardner, S. (2014). Optimal lap altitude for maximum coverage. IEEE Wireless Communications Letters 3 (6): 569–572. 18 Bor-Yaliniz, R.I., El-Keyi, A., and Yanikomeroglu, H. (2016). Efficient 3-D placement of an aerial base station in next generation cellular net- works. 2016 IEEE International Conference on Communications (ICC), pp. 1–5. 19 Bor-Yaliniz, I. and Yanikomeroglu, H. (2016). The new frontier in ran heterogeneity: multi-tier drone-cells. IEEE Communications Magazine 54 (11): 48–55. 20 Hayajneh, A.M., Zaidi, S.A.R., McLernon, D.C., and Ghogho, M. (2016). Drone empowered small cellular disaster recovery networks for resilient smart cities. 2016 IEEE International Conference on Sensing, Communica- tion and Networking (SECON Workshops), pp. 1–6. 21 Gomez, K., Hourani, A., Goratti, L. et al. (2015). Capacity evaluation of aerial LTE base-stations for public safety communications. 2015 European Conference on Networks and Communications (EuCNC), pp. 133–138. 22 Chen, M., Mozaffari, M., Saad, W. et al. (2017). Caching in the sky: proactive deployment of cache-enabled unmanned aerial vehicles for optimized quality-of-experience. IEEE Journal on Selected Areas in Communications 35 (5): 1046–1061. 23 Challita, U. and Saad, W. (2017). Network formation in the sky: unmanned aerial vehicles for multi-hop wireless backhauling. GLOBE- COM 2017 - 2017 IEEE Global Communications Conference, pp. 1–6. 24 Kalantari, E., Yanikomeroglu, H., and Yongacoglu, A. (2016). On the number and 3D placement of drone base stations in wireless cellular networks. 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall). 25 Shakhatreh, H., Khreishah, A., Chakareski, J. et al. (2016). On the con- tinuous coverage problem for a swarm of UAVs. 2016 IEEE 37th Sarnoff Symposium, pp. 130–135.
- 49. Bibliography 23 26 Azari, M.M., Rosas, F., Chen, K., and Pollin, S. (2016). Joint sum-rate and power gain analysis of an aerial base station. 2016 IEEE Globecom Workshops (GC Wkshps), pp. 1–6. 27 Hayajneh, A.M., Zaidi, S.A.R., McLernon, D.C., and Ghogho, M. (2016). Optimal dimensioning and performance analysis of drone-based wire- less communications. 2016 IEEE Globecom Workshops (GC Wkshps), pp. 1–6. 28 Jia, S. and Zhang, L. (2017). Modelling unmanned aerial vehicles base station in ground-to-air cooperative networks. IET Communications 11 (8): 1187–1194. 29 Mozaffari, M., Saad, W., Bennis, M., and Debbah, M. (2016). Efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage. IEEE Communications Letters 20 (8): 1647–1650. 30 Mozaffari, M., Saad, W., Bennis, M., and Debbah, M. (2015). Drone small cells in the clouds: design, deployment and performance anal- ysis. In 2015 IEEE Global Communications Conference (GLOBECOM), pp. 1–6. 31 Mozaffari, M., Saad, W., Bennis, M., and Debbah, M. (2017). Mobile unmanned aerial vehicles (UAVs) for energy-efficient internet of things communications. IEEE Transactions on Wireless Communications 16 (11): 7574–7589. 32 Azari, M.M., Rosas, F., Chen, K., and Pollin, S. (2018). Ultra reliable UAV communication using altitude and cooperation diversity. IEEE Transactions on Communications 66 (1): 330–344. 33 Alzenad, M., El-Keyi, A., Lagum, F., and Yanikomeroglu, H. (2017). 3-D placement of an unmanned aerial vehicle base station (UAV-BS) for energy-efficient maximal coverage. IEEE Wireless Communications Letters 6 (4): 434–437. 34 Alzenad, M., El-Keyi, A., and Yanikomeroglu, H. (2018). 3-D placement of an unmanned aerial vehicle base station for maximum coverage of users with different QoS requirements. IEEE Wireless Communications Letters 7 (1): 38–41. 35 Khuwaja, A.A., Zheng, G., Chen, Y., and Feng, W. (2019). Optimum deployment of multiple UAVs for coverage area maximization in the presence of co-channel interference. IEEE Access 7: 85203–85212. 36 International Telecommunication Union (ITU) (2003). Propagation Data and Prediction Methods for the Design of Terrestrial Broadband Millimetric Radio Access Systems. Technical report. International Telecommunication Union (ITU). 37 3GPP (2018). Study on Channel Model for Frequencies from 0.5 to 100 GHz. Technical report. 3rd Generation Partnership Project (3GPP).
- 50. 24 2 Channel Model for Airborne Networks 38 Wentz, M. and Stojanovic, M. (2015). A MIMO radio channel model for low-altitude air-to-ground communication systems. 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall), pp. 1–6. 39 Gulfam, S.M., Nawaz, S.J., Ahmed, A., and Patwary, M.N. (2016). Anal- ysis on multipath shape factors of air-to-ground radio communication channels. 2016 Wireless Telecommunications Symposium (WTS), pp. 1–5. 40 Zeng, L., Cheng, X., Wang, C., and Yin, X. (2017). Second order statistics of non-isotropic UAV ricean fading channels. 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), pp. 1–5. 41 Blandino, S., Kaltenberger, F., and Feilen, M. (2015). Wireless channel simulator testbed for airborne receivers. 2015 IEEE Globecom Workshops (GC Wkshps), pp. 1–6. 42 Ksendzov, A. (2016). A geometrical 3D multi-cluster mobile-to-mobile MIMO channel model with rician correlated fading. 2016 8th Inter- national Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), pp. 191–195. 43 Gao, X., Chen, Z., and Hu, Y. (2013). Analysis of unmanned aerial vehicle MIMO channel capacity based on aircraft attitude. WSEAS Transactions on Information Science and Applications 10 (2): 58–67. 44 Yang, J., Liu, P., and Mao, H. (2011). Model and simulation of nar- rowband ground-to-air fading channel based on Markov process. 2011 International Conference on Network Computing and Information Secu- rity, Volume 1, pp. 142–146. https://doi.org/10.1109/NCIS.2011.37. 45 Simunek, M., Fontán, F.P., and Pechac, P. (2013). The UAV low ele- vation propagation channel in urban areas: statistical analysis and time-series generator. IEEE Transactions on Antennas and Propagation 61 (7): 3850–3858. 46 Cid, E.L., Alejos, A.V., and Sanchez, M.G. (2016). Signaling through scat- tered vegetation: empirical loss modeling for low elevation angle satellite paths obstructed by isolated thin trees. IEEE Vehicular Technology Maga- zine 11 (3): 22–28. 47 Matolak, D.W. and Sun, R. (2016). Air-ground channels for UAS: sum- mary of measurements and models for L- and C-bands. 2016 Integrated Communications Navigation and Surveillance (ICNS), p. 8B2-1–8B2-11. 48 Sun, R., Matolak, D.W., and Rayess, W. (2017). Air-ground channel char- acterization for unmanned aircraft systems-part IV: airframe shadowing. IEEE Transactions on Vehicular Technology 66 (9): 7643–7652. 49 Kosmerl, J. and Vilhar, A. (2014). Base stations placement optimization in wireless networks for emergency communications. 2014 IEEE Interna- tional Conference on Communications Workshops (ICC), pp. 200–205.
- 51. Bibliography 25 50 Valiulahi, I. and Masouros, C. (2020). Multi-UAV deployment for throughput maximization in the presence of co-channel interference. IEEE Internet of Things Journal 8 (5: 3605–3618. 51 Zhao, N., Pang, X., Li, Z. et al. (2019). Joint trajectory and precoding optimization for UAV-assisted NOMA networks. IEEE Transactions on Communications 67 (5): 3723–3735. 52 Zeng, Y., Zhang, R., and Lim, T.J. (2016). Throughput maximization for UAV-enabled mobile relaying systems. IEEE Transactions on Communi- cations 64 (12): 4983–4996. 53 Zeng, Y. and Zhang, R. (2017). Energy-efficient UAV communication with trajectory optimization. IEEE Transactions on Wireless Communica- tions 16 (6): 3747–3760. 54 Yang, Z., Pan, C., Wang, K., and Shikh-Bahaei, M. (2019). Energy efficient resource allocation in UAV-enabled mobile edge comput- ing networks. IEEE Transactions on Wireless Communications 18 (9): 4576–4589. 55 Mumtaz, S., Huq, K.M.S., Radwan, A. et al. (2014). Energy efficient interference-aware resource allocation in LTE-D2D communication. 2014 IEEE International Conference on Communications (ICC), pp. 282–287. 56 Cui, F., Cai, Y., Qin, Z. et al. (2019). Multiple access for mobile-UAV enabled networks: joint trajectory design and resource allocation. IEEE Transactions on Communications 67 (7): 4980–4994.
- 53. 27 3 Ultra-wideband Channel Measurements and Modeling for Unmanned Aerial Vehicle-to-Wearables (UAV2W) Systems Amit Kachroo1 , Surbhi Vishwakarma1 , Jacob N. Dixon2 , Hisham Abuella1 , Adithya Popuri1 , Qammer H. Abbasi3 , Charles F. Bunting1 , Jamey D. Jacob4 , and Sabit Ekin1 1 School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA 2 IBM, Rochester, MN, USA 3 School of Engineering, University of Glasgow, Glasgow, UK 4School of Mechanical and Aerospace Engineering, Oklahoma State University, Stillwater, OK, USA 3.1 Introduction Over the past decades, wireless technology has seen an upward trend in the bandwidth of the signals employed. The main reason behind this upward trend is the proliferation of multimedia technologies that demand high data rate, and also an increase in user base. Ultra-wideband (UWB) radio is one of the creations of this trend where the bandwidth occupied by UWB tech- nology is greater than or equal to 500 MHz. Therefore, UWB communication technology exploits this large bandwidth to catch up with the high data rate. Apart from the high bandwidth, the main advantages of UWB can be listed as follows: ● Low power consumption with high data rate. The received power in UWB lies very close to the noise floor [1–5]. ● Control over duty cycle makes the battery last longer. ● Low probability of detection as it is close to the noise floor and any attempt of jamming or eavesdropping will make the signal noisy [6]. ● Small wavelength with low power makes it a perfect fit for body-centric wireless network [3, 4]. Given these advantages, UWB is best suited for off-body communication. Moreover, the Federal Communication Commission’s (FCC) guideline of the Autonomous Airborne Wireless Networks, First Edition. Edited by Muhammad Ali Imran, Oluwakayode Onireti, Shuja Ansari, and Qammer H. Abbasi. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.
- 54. 28 3 Ultra-wideband Channel Measurements and Modeling power limit of −41.3 dBm or 75 nW/MHz identifies the UWB technology as an unintentional interference source; the fact that it can thereby coex- ist with other wireless technologies, especially at 2.4 GHz (WiFi, Bluetooth) with minimal or no interference, reinforces the application of UWB tech- nologies for off-body communication. On the other hand, unmanned aerial vehicles (UAVs) are being now used for remote healthcare deliveries especially to far flung areas that lack connectivity. UAVs are also being used for emergency medical deliveries where time is of utmost importance, such as during cardiac arrests [7–10]. One of the upcoming themes for UAVs is to directly monitor the health of a patient by utilizing wearable patch devices [1, 7, 10–12]. The study in this chapter explores the UWB technology with UAVs further for health monitoring applications. This type of setup involving UAV and wearable antenna/antennas is also known as unmanned aerial vehicle-to-wearable (UAV2W) systems [1]. The closest one to this study is our previous work [1], where different UWB bandwidths were considered for channel modeling in an indoor envi- ronment. However, in this work, we consider the complete UWB bandwidth of 7.5 GHz to study these body channels, and also to look into two different environments and study the effect of postures. Also, previous studies such as [5, 13–15] have performed on-body radio channel characterization and modeling at 2.45 GHz but not at the UWB frequency. In addition [16–18] per- formed off-body radio channel studies in a contained scenario with antennas placed in standalone position. The other closest study is in [2, 3], where off- and on-body channel characterizations are performed without the real human subject. To the best of our knowledge, this is one of the first works to consider UWB channel characterization between humans and UAV at 7.5 GHz bandwidth, and has studied different environments and the effects of different body postures on the UWB system. The rest of the chapter is organized as follows: Section 3.2 discusses the measurement setup and data acquisition part, and Section 3.3 covers the UWB-UAV2W radio channel characterization. Section 3.4 details the statis- tical analysis and finally, Section 3.5 presents the conclusion based on the measurement campaign done so far. 3.2 Measurement Settings There are generally two methods to measure the channel response in a wire- less communication, either time correlator based or frequency sweep based. In our work, we have utilized the latter one by using a Vector Network
- 55. Another Random Scribd Document with Unrelated Content
- 56. back
- 58. back
- 60. back
- 62. back
- 64. back
- 66. back
- 68. back
- 70. back
- 72. back
- 74. back
- 76. Welcome to our website – the perfect destination for book lovers and knowledge seekers. We believe that every book holds a new world, offering opportunities for learning, discovery, and personal growth. That’s why we are dedicated to bringing you a diverse collection of books, ranging from classic literature and specialized publications to self-development guides and children's books. More than just a book-buying platform, we strive to be a bridge connecting you with timeless cultural and intellectual values. With an elegant, user-friendly interface and a smart search system, you can quickly find the books that best suit your interests. Additionally, our special promotions and home delivery services help you save time and fully enjoy the joy of reading. Join us on a journey of knowledge exploration, passion nurturing, and personal growth every day! ebookbell.com