UAV Placement and Trajectory Design Optimization: A Survey

Abstract

Unmanned Aerial Vehicles (UAVs) have recently attracted attention in military areas as well as a wide range of commercial and civilian applications. With UAVs equipped with advanced transmitters and sensors, and with high mobility and flexibility in deployment, they have gained a special place in the field of information technology. Since there are several types of UAVs available, depending on circumstances choosing an appropriate one is essential for proper use of them. In this paper, we review some types of UAVs and a variety of UAV-enabled wireless networks with a focus on optimizing UAV position and flight paths. Since using UAVs is considered as one important complement for future cellular networks like 5G and Beyond 5G networks in disasters we focus on UAV placement optimization in cellular networks and then, we investigate the optimal UAV location for various scenarios and applications, and introduce methods of UAVs placement and trajectory design optimization.

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Wireless Personal Communications
https://doi.org/10.1007/s11277-021-09451-7
1 3
UAV Placement andTrajectory Design Optimization: ASurvey
AhmadMazaherifar1· SeyedakbarMostafavi1
Accepted: 21 November 2021
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
Abstract
Unmanned Aerial Vehicles (UAVs) have recently attracted attention in military areas as
well as a wide range of commercial and civilian applications. With UAVs equipped with
advanced transmitters and sensors, and with high mobility and flexibility in deployment,
they have gained a special place in the field of information technology. Since there are
several types of UAVs available, depending on circumstances choosing an appropriate one
is essential for proper use of them. In this paper, we review some types of UAVs and a
variety of UAV-enabled wireless networks with a focus on optimizing UAV position and
flight paths. Since using UAVs is considered as one important complement for future cel-
lular networks like 5G and Beyond 5G networks in disasters we focus on UAV placement
optimization in cellular networks and then, we investigate the optimal UAV location for
various scenarios and applications, and introduce methods of UAVs placement and trajec-
tory design optimization.
Keywords Unmanned Aerial Vehicles· Placement optimization· Trajectory design· Path
planning
1 Introduction
UAVs have recently attracted attention in military areas as well as a wide range of com-
mercial and civilian applications, including traffic control [1], cargo delivery [2], security
[3], aerial inspection [4, 5], search and rescue [6], video streaming [7], precision farming
[8], surveillance [9], aerial photography [10], etc. For more than 25years, UAVs have been
used for military applications such as reconnaissance, border protection [11], and attack,
also organizations such as the police department [12], social security, and transportation
use UAVs for civil application. UAVs can be used in operations such as long-term moni-
toring that are tedious [13], or the use of highly polluting pesticides, or dangerous rescue
operations. Recent advances in the field of electronics and sensors have expanded UAV
applications to areas such as traffic control, wind speed estimation and remote sensing
* Seyedakbar Mostafavi
a.mostafavi@yazd.ac.ir
Ahmad Mazaherifar
hmd.mzhr@stu.yazd.ac.ir
1 Department ofComputer Engineering, Yazd University, Yazd, Iran

A.Mazaherifar, S.Mostafavi
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[14]. UAVs can alarm disasters in a timely manner and help to speed up search and res-
cue operation insituations where communication infrastructures are destroyed. With UAVs
equipped with advanced transmitters and sensors, and with high mobility and flexibility in
deployment, they have gained a special place in the field of information technology. With
the ever-increasing costs of UAVs and government efforts to regulate the use of UAVs,
the demand for UAVs is expected to grow dramatically. The global market for UAVs is
estimated at $2 billion in 2016, reaching $127 billion in 2020 [15]. New rules have been
approved by the Federal Aviation Administration for commercial applications of UAVs,
which is expected to generate $82 billion for the US over the next decade. [16, 17]. Several
leading IT companies follow pilot projects such as the Facebook Aquila project and Google
Loon, using UAVs for massive Internet access around the world. One of the recent efforts
to recover network with UAVs is the European Commission project ABSOLUTE, which
focuses on low altitude platforms [18].
2 Types ofUAVs andUAV‑Enabled Wireless Networks
Due to the uses and objectives of the wireless networks, the Appropriate UAV should be
used to comply with the rules and environment and provide right quality of service. For the
proper use of a UAV on any wireless network, it is necessary to consider several factors
such as UAV capabilities, flight altitudes, and flight mechanism.
In terms of flight altitude, UAVs can be divided into two general categories: low altitude
platforms (LAP) and high altitude platforms (HAP). HAPs such as balloons have altitudes
of more than 17km and are usually quasi-stationary. On the other hand, LAPs can fly at
altitudes ranging from a few ten of meters up to several kilometers and are highly flexible.
Compared to HAPs, deployment of LAPs is much faster and is more suitable for quick
applications and sensitive situations, and can be used to collect data from sensor networks
[19]. In addition, LAPs are capable of recharging and replacing. in contrast to LAPs, HAPs
are more durable and designed for long-term use. Usually HAPs are used to cover wide-
spread wireless networks in large areas [20, 21]. But HAPs are costly and their deployment
is very time consuming.
In terms of mobility and flight mechanism, the UAVs can be divided into two groups:
fixed-wing UAVs and rotary-wing UAVs. The fixed wing UAVs need to be constantly
moving forward in order to stay in the air. Compared to the fixed-wing UAVs, rotary-wing
UAVs can take-off and land vertically, which makes it easy to use. In addition, rotary-wing
UAVs hover or move in any direction, allowing them to easily stay at optimal points or
move toward them.
Depending on the used mobility type of UAV, two different research paths are defined:
(1) UAV-enabled wireless networks with static UAVs; (2) UAV-enabled wireless networks
with mobile UAVs [22]. Researches on networks enabled with static UAVs usually focuses
on optimizing the deployment or location of UAVs as a quasi-stationary base stations for
serving users in a specific area as well as allocating communication resources [23]. Here,
the altitude and horizontal position of the UAV can be optimized both jointly and sepa-
rately [24, 25]. In networks enabled with mobile UAVs, one can use all the capabilities
of a UAV-enabled wireless networks. Due to the full control of the UAV’s mobility, with
the proper design of the UAV’s trajectory and the communication scheduling, the distance
between the UAV and ground users can be greatly reduced. Trajectory design is done in
two ways: optimizing waypoint or flight angle.

UAV Placement andTrajectory Design Optimization: ASurvey
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In terms of drone numbers, UAV-enabled wireless networks can be divided into single-
UAV networks and multi-UAV networks. Early UAV-enabled wireless networks consisted
of only a single large UAV, and usually used for military applications. Single-UAV net-
works are still very popular due to the simplicity of its communication structure. Today,
UAV-enabled networks for most civilian applications include several small and low-cost
UAVs that UAVs operate in a coordinated manner.
With regard to the environment, UAV-enabled wireless networks can be divided into
networks with outdoor users and networks with indoor users. 70 percent of Internet access
is in indoor spaces, and there are a lot of users in high-rise buildings. When a building is
in disaster and cellular network is interrupted, a UAV can be used as a platform to cover
indoor users. Users need to communicate more securely in high-rise buildings than ter-
restrial users because they are in more dangerous condition. Because of two reasons Con-
ventional models of path loss between the UAV and users are not suitable for indoor envi-
ronment: (1) Users are in the interior, and the path loss for the link between the UAV and
the indoor users includes three components: outdoor path loss, building penetration path
loss, and indoor path loss, (2) The position of each internal user is 3-D, but the position of
outdoor user is 2-D. In [26] The problem of UAV placement optimization with the goal of
minimizing the total energy needed to cover indoor users have being studied.
3 Challenges ofUAV‑Enabled Wireless Networks
Although the use of UAVs for wireless networks is a promising technology, it is a new field
of research, and there is a need for a lot of problems to be solved to ensure reliable utiliza-
tion of UAV-enabled wireless networks. For example, when using a UAV as a base station,
key design issues may include optimal deployment of UAV in 3-D space, trajectory optimi-
zation, wireless and processing resource allocation, and flight duration. Some of the issues
that the implementation of UAV-enabled wireless networks may faces include: UAV place-
ment optimization, trajectory design, channel modeling, energy constraints, flight duration
constraints, performance analysis, security issues, interference management, and backhaul
connection. Several survey articles have examined various aspects of the UAV-enabled
wireless networks [22]. As shown in [6 tab. 2] subjects of these studies include UAVs in
IoT [27], broadband networks using HAPs [28], network requirements of UAVs for civil
applications [29], routing strategies [16], UAV channel modeling [30], studying interfer-
ence and path loss of connection in UAVs [31], and UAV applications [32]. One of the
major issues in UAV-enabled wireless networks is UAV placement optimization and trajec-
tory design. The UAV placement optimization is difficult due to the dependence on many
factors such as the environment, users’ positions and channel characteristics between users
and UAVs, which is a function of the altitude of the UAV. There are several approaches
to address the UAV placement optimization and trajectory design problems to connect or
improve network communication performance. The purpose of this paper is to present the
latest methods and approaches for optimizing UAV position and optimal path planning. In
the next section, we study communication links and the channel models of the UAV-ena-
bled wireless networks. Then, in Sect.5, we introduce UAV applications in wireless net-
works. In Sect.6 we specifically investigate the optimal placement of the UAV in cellular
networks. In Sect.7, different approaches proposed for optimization in previous studies are
presented. In Sect.8, open issues and the challenges of placement optimization and path
planning of UAV are briefly outlined. Finally, Sect.9 is the conclusion.

A.Mazaherifar, S.Mostafavi
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4 Communication Link andChannel Model
Using UAVs, the likelihood of a Line of Sight (LOS) without fading increases in down-
link and uplink communications that increases link reliability and ultimately boosts
profits and resource savings. Therefore, using UAVs for wireless networks is interesting
to service providers and operators. Practically LOSs between ground nodes are usually
blocked by buildings that have an impact on signal strength so that terrestrial radio com-
munication is unreliable and inefficient. UAVs can be deployed quickly at anytime, any-
where, and also change their position to fix the LOS blocking problem. UAV-enabled
wireless networks, due to the LOS communication link with terrestrial terminals, can
provide more capacity than existing technologies such as conventional small-cell net-
works and satellite networks.
4.1 Communication Link
Communication links in UAV-enabled wireless networks generally include two basic links:
control and con-payload communications link, and data link [17, 22, 30, 33]. To realize the
large scale UAVs deployment, the most important issue is to ensure the safety of the UAVs,
which requires an extremely reliable link between the UAVs and their control stations. The
control and non-payload communications (CNPC) link is essential to ensure proper opera-
tion of UAV systems, and should be highly reliable, with a low latency, and a full-duplex
secured low-rate data connection. This link is used to send critical data between UAVs
and UAVs with the ground control center. Since this link is critical, a protected range
should be used, with two bands assigned to this link: L-band (960–977MHz) and C-band
(5030–5091MHz).
Depending on the scenario, the data link can be established between the UAV and the
base stations, mobile terminals, wireless sensors etc. The capacity of this link is highly
dependent on its application. The capacity of this link varies from a few kilobits per second
in sensor networks to several gigabits per second in the backhaul communications. Com-
pared to the CNPC link, this link is more security and latency tolerant.
4.2 Communication Channel
Both CNPC and data link include two types of channels: UAV-ground channel and UAV-
UAV channel. Usually in the UAV-ground channel, we expect to have a LOS link, but
this link may be blocked by obstacles such as buildings and terrestrials. Also, for LAPs,
this channel may include multipath components scattering, etc. For environments such
as deserts and seas, the LOS model is considered, and another model commonly consid-
ered for other environments is the Rician model with a LOS component. One of the com-
mon path loss models used in most researches as a base model is the model presented by
al-Hourani etal. For LAPs [18]. This model is a stochastic model in which the pathloss
between ground user and UAV depends on UAV and ground user position and propagation
environment. In this model, depending on the conditions, the link can be both LOS and
non-LOS (NLOS). Probability of LOS and NLOS in this model is considered as follows:
(1)
𝐏(LOS,𝜃)=1−𝐏(NLOS,𝜃)

UAV Placement andTrajectory Design Optimization: ASurvey
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where θ is the elevation angle and P(LOS,θ) is the probability of occurrence of LOS,
which strongly depends on the elevation angle. Using the parameters that the International
Telecommunication Union (ITU) provides for a LOS probability between a transmitter and
receiver with a specified height, the LOS probability is estimated to be as follows:
where C and B are constants that depends on environment. exploiting the concept of coor-
dinate multipoint, Liu et al. proposed a new architecture for UAV-enabled wireless net-
works called coordinate multipoint in the sky and introduced a model for communication
channel which is a LOS model with random phase [34]. Also, considering the position of
indoor users, the ITU-R model is used to model outdoor-indoor pathloss [35].
UAV-UAV channel model is often considered a LOS component. Although multipath
components may be presented due to the surface reflection of the Earth, compared to the
UAV-ground channel its impact is negligible.
5 UAV Applications inWireless Networks
The UAV-enabled wireless networks can be divided into three category depending on the
application of the UAV in the network: (1) UAV-aided ubiquitous coverage, (2) UAV-aided
relaying, (3) UAV-aided information dissemination [22, 36].
5.1 UAV‑Aided Ubiquitous Coverage
Here, a UAV is used to assist the ground telecommunication infrastructures to fully cover
a service area. In this case, as shown in Fig.1, UAVs are typically quasi-stationary above
the target range used as a base station to quickly retrieve the network after a complete or
partial failure of the infrastructure. Compared to ground stations, UAVs are deployed in a
(2)
𝐏
(LOS,𝜃)=
1
1+Cexp(−B[𝜃−C])
Fig. 1 UAV base station

A.Mazaherifar, S.Mostafavi
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3D space, and due to their mobility, they are usually fixed for a short time. UAVs also have
fewer restrictions on location. UAV base station can be used for data offloading. There-
fore, we can use UAVs as mobile cloudlets that provide offloading for mobile users. Hence,
UAVs can make fog computing available even in the absence of wireless infrastructure.
Mobile users can offload their heavy tasks such as object recognition or augmented reality
applications via ground-to-air communications and air-to-ground communications to the
cloudlet. The offloading operation consists of three steps: (1) transfer of application data
to the UAV using ground-to-air data, (2) processing of data in the cloudlet, (3) transfer of
processing outputs from the cloudlet to mobile users using air-to-ground communications.
The authors in [37] consider UAVs as a cloudlet in wireless network that allows mobile
users to offload their data. UAV trajectory and bit allocation are also optimized to minimiz-
ing energy consumption.
5.2 UAV‑Aided Relaying
Most of the research conducted on the optimal location of UAVs as a base station offers
useful insights on optimal UAV location. However, the missing point is that the UAV’s
storage and processing power is limited. As a result, data must be relayed to a ground
base station. As shown in Fig.2, UAV-aided relay networks provide wireless connectiv-
ity between two or more remote users or a group of users who do not have a reliable direct
connection, such as the connection between the command center and the frontline. In [38],
authors investigate UAV relay optimal altitude where UAV is static or moving in a circle
path with focus on energy consumption, probability of outage, and bit error rate. Here,
optimum altitude is obtained for both Amplify-and-Forward and Decode-and-Forward
relays, and numerical results show that the Decode-and-Forward relay have better perfor-
mance. In [39], with the aim of maximizing throughput, the problem of energy allocation
at source and UAV relay, and the flight path, is optimized by considering the relay speed
and the position of ground users constraints. Numerical results show that the use of UAV
relays with an optimal trajectory design significantly increases the throughput. In [40], a
UAV-aided relaying system with half-duplex communication consisting of a base station,
a UAV, and a mobile device is considered, where UAV acting as an Amplify-and-Forward
relay. In this paper, with the goal of minimizing outage probability, trajectory design and
power control are optimized jointly. In [34] UAVs are used as coordinated flying Remote
Radio Heads, whose mission is to relay user information to the central processor. In this
paper, the flight path and location of the UAV depending on the movement of users will
be designed and updated over time, in order to maximize the coverage of the users and the
average throughput over time.
Fig. 2 UAV relay

UAV Placement andTrajectory Design Optimization: ASurvey
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5.3 UAV‑aided Data collection orInformation Dissemination
In the Internet, objects and wireless sensor networks can use UAVs to collect data, which
reduces the power consumption of sensors and, consequently, longevity of the network.
Usually, the data collection center in traditional wireless sensor networks is considered to
be static. The difference between sensors-to-center distances causes different transmitting
powers requirement for reliable data transmission. This causes heterogeneous energy con-
sumption of sensors that ultimately reduces the lifetime of wireless sensor networks. Using
UAVs in wireless sensor networks seems to be a promising solution to data collection. Fig-
ure 3 shows the use of UAVs in wireless sensor networks to collect data from sensors.
Two things should be noted when designing UAV trajectory in wireless sensor networks:
(1) To establish a LOS link, the UAV should be close enough to the sensors. (2) All sen-
sors should be covered with a given time range. In [41], with the goal of minimizing the
maximum energy consumption of sensors, UAV trajectory and sensors’ wakeup schedul-
ing are jointly optimized. In [42], considering the distribution of the parameters that the
sensors sense, and with the aim of minimizing parameters mean squared error, UAV tra-
jectory optimized so that the maximum number of sensors is covered. In [43], UAVs are
considered as a base station for collecting data from IoT ground devices. Here the UAV
trajectory is optimized such that IoT devices have a reliable connection with the UAV with
minimum energy consumption. First IoT devices are clustered considering that they are
fixed and transmission power is minimized by optimizing the position of the UAV over
clusters. Then the UAV trajectory is optimized to establish an effective connection between
the UAV and mobile IoT devices.
6 Using UAVs inCellular Networks
To integrate UAVs and cellular networks, there are two paradigms: (1) UAV-assisted cel-
lular communication, (2) Cellular-enabled UAV communication [33, 44]
Fig. 3 UAV data collection

A.Mazaherifar, S.Mostafavi
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6.1 UAV‑Assisted Cellular Communication
Most researches aimed at optimal UAV placement and UAV trajectory design focus on
the use of UAVs as base stations or relays in cellular networks. Optimizing placement
of UAV base stations is performed with different goals in cellular networks. For exam-
ple, in [45], a microcell network is considered where UAV base station relies on it to
communicate with the core of the network. In the paper, 3D UAV position and band-
width allocation are optimized to maximize profitability for servers. The authors in [46]
optimize the UAV base station position in environments with different user densities,
with the goal of fully covering users with the minimum number of UAV base stations
required. In [47] The optimal location of the UAV station is aimed at maximizing the
coverage of users with different quality of service. UAVs in cellular networks are also
used as relays. For example, in [48], a UAV is used as a flying relay to bridge the gap in
terms of coverage and capacity.
6.1.1 On‑demand UAV Placement Optimization
Compared to conventional small-cell networks, with a large number of fixed small-cells
distributed in a cell, using UAV small-cells and exploiting UAV mobility, distance with
the edge-users of the cell shortens, and thus Implementation and installation costs are
expected to fall considerably [49]. Unlike fixed small-cell networks, deployment of
UAV systems can be done on-demand. In [50] a model is presented for optimizing the
placement of multiple UAVs based on user demand with the goal of increasing capac-
ity and reducing delay. In this model, a cost function and density function is assigned
to each area based on the demand pattern of users. Using this functions and a neural
model, each UAV is matched to a demand zone. In [51], to provide wireless service
based on the users’ demand in cellular networks, a machine learning framework based
on the Gaussian Mixture Model and Weighted Expectation Maximization algorithm
are used to predict network congestion. The optimal UAV position is obtained using
the predicted results to minimizing the energy needed for UAV movement and data
transmission.
6.2 Cellular‑Enabled UAV Communication
A lot of research has been done on the enabling wireless and cellular networks using
UAVs, and the research on it have been started much earlier than the second paradigm. In
contrast, research on the use of UAVs as cellular network users, despite its high potential,
is still in its infancy. Aerial users of cellular networks are one of key factors for the Internet
of things. Amazon, for example, uses UAVs connected to cellular networks for cargo deliv-
ery. In [33], UAV trajectory design optimization is investigated where the UAV connects
to cellular networks through ground base station along its flight path. The UAV mission is
from a starting point to the destination. The UAV connects to the base station with the best
channel. UAV trajectory design is optimized to minimize mission completion time accord-
ing to minimum SNR constraint. In [52], the path planning of cellular connected UAV with
disconnectivity constraint is studied. The UAV’s mission is to fly from starting point to a
destination without being disconnected more than a given duration.

UAV Placement andTrajectory Design Optimization: ASurvey
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6.3 UAVs andBeyond 5G Networks
With the appearance of 5G technology, network connectivity is expected to be seamless
and ubiquitous, supporting at least 1000-fold traffic volume for 100 billion wireless-con-
nected devices. Contrary to the current 4G network, the characteristics of latency, reliabil-
ity, battery life, etc. are diversified in 5G. Today, the popularity of IoT has led to a signifi-
cant increase in mobile data traffic [53]. Using UAVs as a complement to 5G networks in
emergencies and disasters is very noticeable. so UAVs are one of the main components
of the 5G and B5G networks. Figure4 shows a 3D architecture of the B5G UAVs that
includes UAV base stations, drone user-equipment (drone-UEs) and HAPs [54]. In this
architecture, the focus is on aerial networks, which only include UAVs that UAV base sta-
tions serve drone-UEs, and.
HAPs provide a backhaul wireless connection. UAV base stations communicate with
their users using FDMA technique. The proposed framework for designing a 3D cellular
network in this architecture involves UAV base stations placement, estimating the spatial
distribution of drone UEs using machine learning tools, and ultimately connecting users to
less-delayed UAV base stations using optimal transport theory [54].
7 UAV Placement Optimization Methods
UAV placement and trajectory design optimization involves two objectives: (1) increasing
network revenue (coverage, reachable rate, etc.) under certain constraints like transmission
power, flight duration, number of UAVs, etc. (2) reduce the cost of deployment (transmis-
sion power, flight duration, number of UAVs) to achieve desired quality of service [55].
In [47], considering a set of service qualities and assigning a service quality to a user,
3D position of the UAVs are optimized to maximize the coverage of users with different
Fig. 4 Beyond 5G UAV cellular
network [54]

A.Mazaherifar, S.Mostafavi
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quality of services. UAV placement and trajectory design optimization is done with two
approaches: (1) using mathematical programming techniques and (2) machine learning. In
the first approach, the problem of UAV positioning is formulated as a programming prob-
lem and solved by existing mathematical methods. The optimization formulas obtained are
usually mixed-integer non-convex problems and are therefore NP-hard issues. In the fol-
lowing, a trajectory design optimization problem formulated to minimize flight time of the
drone is shown where T is the time of flight. 3a and 3b constraints specify the average rate
requirement for downlink and uplink and 3i constraints ensures that the UAV returns to the
initial location at the end of each period [56].
To solve these problems, algorithms based on iterative methods such as block coordinate
descent and successive convex approximation or heuristic and metaheuristic algorithms such
as genetic algorithm, simulated annealing, particle swarm optimization, ant colony, and so on
are used. By using block coordinate descent and successive convex approximation, authors in
[57] propose an iterative algorithm that optimizes the UAV trajectory design and transmission
power jointly in each iteration. It is shown that the proposed algorithm is convergence to an
optimal solution. The authors in [58] exploiting the UAV’s mobility, jointly optimize UAV
trajectory design and transmission power to provide link security between UAVs and ground
users against eavesdropping. In this research, to solve optimization problem an iterative algo-
rithm is presented using block coordinate descent and successive convex approximation. The
results show that the proposed method provides more confidentiality than other methods.
The authors in [59] to ensure that all users are connected with a UAV relay with the lowest
(3a)
min T
s
.t.B
T
T
�
0
Ru
i(t)dt ≥R
u
i,∀i∈U
,
(3b)
B
T
T
�
0
Rv
i(t)dt ≥R
v
i,∀i∈V
,
(3c)
V
∑
j=1
pj(t)≤Pv,∀t
,
(3d)
pj(t)≤0, ∀j,t,
(3e)
U
∑
i=1
𝛼i(t)+
V
∑
j=1
𝛽j(t)≤1, ∀t
,
(3f)
𝛼i(t)≥0, ∀i,t,
(3g)
𝛽i(t)≥0, ∀j,t,
(3h)
q(t)≤Vmax,∀t
(3i)
q(0)=q(T),

UAV Placement andTrajectory Design Optimization: ASurvey
1 3
number of links and costs, knowing the users’ positions, they propose an algorithm based on
the particle swarm optimization method to find the optimal position of the UAV relay. Also,
the authors of [46] utilize the particle swarm optimization algorithm, under capacity constraint
obtain the minimum number of UAV base stations required to cover the users. Then, using the
obtained answer, the UAV base stations positions is optimized. In the second approach, which
is based on machine learning, the optimization problem is considered as a clustering problem
where each cluster consists of a set of users that are assigned to a UAV. In [60], authors pro-
pose a clustering and parameter estimation method where energy measurement samples of the
air-to-ground radio map are used to learn and predict the optimal position of the UAV.
The iterative methods like block coordinate descent are widely used to optimize the place-
ment and resource allocation for single or multiple UAVs [47, 61, 62]. This method is compu-
tationally efficient but it may result in a low-quality local optimum. Geometry-based methods
including dynamic clustering algorithm [63] and circle packing method [64] are frequently
applied to optimize the UAV placement and power consumption. However, these methods
may fail in a complicated situation [65]. The graph theory-based design framework for trajec-
tory optimization may result in low complexity and good performance [33]. Another com-
mon approach is to apply different algorithms concurrently. For example, combining the
block coordinate descent method, the concave-convex procedure, and the alternating direc-
tion method of multipliers show low computational complexity [65]. Recently, artificial intel-
ligence and machine learning techniques have demonstrated promising performance gains and
complexity reduction in wireless networks. Among the various machine learning methods,
reinforcement learning is extensively used for wireless network optimization [66].
8 Open Issues forFuture Research
As shown in the Table1 many researches have been conducted on the optimal location of
UAVs in wireless and cellular networks. The focus of most of these studies is on the use of
UAVs as base stations or relays to provide access to wireless networks for users, and very little
research has been done to optimize the position and flight path of UAVs as users of wireless
and cellular networks. One of the issues that can be addressed in using UAVs in cellular net-
works as a user is designing UAV trajectory such that does not allow the UAV to enter areas
where there is no cellular network coverage.
Another open issue in the field of UAVs is the use of UAV-enabled wireless networks in
the presence of terrestrial wireless networks such as cellular networks. In this case, the drone
location should be optimized to avoid interference with terrain networks.
One of the successful and emerging approaches in wireless networks is the use of machine
learning algorithms for resource allocation as well as network status prediction. As noted
above, there is limited research in this area and it is expected that the use of this method will
be of great interest to researchers with the development of learning algorithms and increasing
the processing power of drones.
9 Conclusion
In this paper, we have investigated the problem of UAV placement and trajectory design
optimization in wireless and cellular networks. In this paper we studied the factors affect-
ing UAV placement, such as channel model between the UAV and the users, and the most

A.Mazaherifar, S.Mostafavi
1 3
Table 1 Objectives of UAV placement optimization problem and proposed solutions
Refs. # Objective UAV application Mobility Solution
[18] Providing maximum radio coverage Aerial base station Stationary UAV Modeling LOS probability and using it
to find the optimal altitude
[26] Minimizing the total transmit power
required to provide wireless coverage
for indoor users
Aerial base station Stationary UAV Using a mathematical approach, they
Find the UAV 3-d coordinates to cover
the location that has the highest path
loss
[34] Introducing a new architecture to
mitigate inter-user interference also
maximizing the minimum of average
user rates under different application
scenarios
Aerial remote radio heads and remote
aggregation units in coordinate multi-
point systems
Stationary and mobile UAV Propose a realistic LOS channel model
this paper formulate the deployment
problem and derives a closed form
approximation of objectives by apply-
ing random matrix theory
[35] Maximize the indoor wireless coverage Aerial base station Stationary UAV Formulate the problem utilizing circle
packing problem and solve it as some
decision problem
[36] Rate-maximization or energy-mini-
mization for energy efficient UAV
communication
UAV is employed to send information
to a ground terminal
Mobile UAV Based on two techniques: discrete linear
state-space approximation and sequen-
tial convex optimization this paper
proposes an efficient algorithm to solve
constrained trajectory problem
[38] Maximizing reliability which is
considered as the total power loss,
the overall outage, and the overall bit
error rate
Aerial relay Stationary UAV or mobile UAV flying
in a circle with radius r
Leveraging the path loss model proposed
in [18] and using numerical analysis
this paper finds the UAV optimal Alti-
tude maximizing the reliability
[39] Maximizing throughput in mobile
relaying systems by optimizing the
source/relay transmit power along
with the relay trajectory subject to
UAV’s speed and initial/final relay
locations
Aerial relay Mobile UAV An iterative algorithm is proposed to
optimize the power allocations and
relay trajectory
[40] Minimizing the outage probability of
the relay network
Amplify-and-forward relay Mobile UAV Using Alternating Minimization an itera-
tive algorithm is proposed to optimize
power control and trajectory jointly

UAV Placement andTrajectory Design Optimization: ASurvey
1 3
Table 1 (continued)
Refs. # Objective UAV application Mobility Solution
[41] Minimizing the maximum energy
consumption of all SNs
Data collector Mobile UAV By applying the successive convex opti-
mization technique, an efficient itera-
tive algorithm is proposed to solve the
mixed integer non-convex programing
problem
[42] Minimizing mean square error for the
estimated parameter by collecting
data from as many SN as possible
Data collector Mobile UAV Using the traveling salesman problem,
an efficient suboptimal solution is
proposed for NP-hard trajectory design
problem
[45] Maximizing the profitability of service
provided in terms of achievable data
rate levels
Aerial base station Stationary UAV This paper formulates the problem as an
mixed-integer nonlinear programming
problem then reduces it to an Integer
Knapsack Problem and proposes
a novel search algorithm using the
Golden Section Search to solve it
[46] Minimizing the number of UAVs that
is required to cover all users
Aerial base station Quasi Stationary UAV This paper leverage the LOS model pro-
posed in [18] to formulate the problem
and propose a heuristic algorithm
based on particle swarm optimization
to solve it
[47] Maximizing the number of covered
users with different Quality-of-Ser-
vice requirements
Aerial base station Stationary UAV This paper formulates the 3-d placement
problem as an mixed-integer nonlinear
programming problem. To simplify the
problem, the authors decuple vertical
and horizontal placement. They use
an exhaustive search algorithm to find
the optimal altitude and also propose
a low complexity algorithm called the
maximal weighted area

A.Mazaherifar, S.Mostafavi
1 3
Table 1 (continued)
Refs. # Objective UAV application Mobility Solution
[48] Maximizing the end-to-end throughput
and minimizing transmission power
Aerial relay Stationary UAV The authors in This paper uses the user’s
received power map corresponding
to every UAV position and propose a
bisection search algorithm to find the
optimal UAV position
[49] Maximizing the minimum throughput
of all mobile terminals by jointly
optimizing the UAV’s trajectory,
bandwidth allocation and user
partitioning
Aerial base station Mobile UAV flying in a circle with
radius r
This paper reduces the formulated
problem to a series of subproblems and
solve them using bisection search
[50] Achieving a reliable and load balanced
future 5g macro cell and small cell
network where user demand is greater
than available capacity
Aerial relay between Macro cell Base
Stations and small cell UEs
Quasi Stationary UAV This paper provides a cost function of
delay, availability of LOS, and cover-
age and also propose density functions
that quantifies population of active
users. Using a neural model UAVs are
assigned to hotspots
[51] Minimizing the power required for
transmission and UAV mobility
Aerial base station Quasi stationary UAV Propose a novel machine learning model
to predict congestion and hotspot
events and deploy UAV BSs to provide
service to mobile users
[52] Minimizing trip distance subject to
connectivity constraint
Cellular-connected UAV performing
various missions
Mobile UAV Propose an approximate solution based
on dynamic programing
[54] Minimizing number of UAVs while
ensuring full coverage of a 3-D space
Aerial base station in a 3-D architec-
ture
Stationary UAV The authors first fill the given space with
truncated octahedron then place each
UAV at the center of each of truncated
octahedron

UAV Placement andTrajectory Design Optimization: ASurvey
1 3
Table 1 (continued)
Refs. # Objective UAV application Mobility Solution
[56] Minimizing mission completion time Data collection in the uplink, data
transmission in the downlink, and
data relaying between GUs
Mobile UAV This paper uses successive convex opti-
mization and block coordinate descent
techniques to find the optimal trajec-
tory and leveraging traveling salesman
problem and pickup-delivery problem
to design the initial trajectory
[59] Improving the network connectivity
and communication performance
Aerial relay Stationary UAV Using a minimum spanning tree this
paper proposes a cost function of three
communication metrics and LOS, and
solves the problem utilizing particle
swarm optimization
[37] Minimizing the total energy consump-
tion of mobile users while satisfying
quality of service requirements
Aerial cloudlet Mobile UAV Using successive convex approximation
strategies
[62] Minimizing the number of MBSs to
cover Maximum number of ground
terminals
Aerial mobile base station Mobile UAV The authors propose a heuristic algo-
rithm named spiral MBSs placement
based on the successive placement of
UAV spirally
[67] Minimize the average GT power
consumption
Due to the GTs density variation over
time: Stationary UAV, quasi station-
ary UAV
For static user density, the authors
use quantization theory and find an
approximate optimal solution for a
large number of UAVs and they use
the Lagrangian approach for dynamic
scenarios and propose an algorithm to
ensure the convergence of Lagrangian
[68] Improving energy efficiency and reduc-
ing redundant data
Data collector Mobile UAV This paper uses simulated annealing
algorithm to plan the path of UAV
based on the selected sampling points
that are obtained based on matrix
completion

A.Mazaherifar, S.Mostafavi
1 3
Table 1 (continued)
Refs. # Objective UAV application Mobility Solution
[69] Maximizing the system throughput Aerial relay Stationary UAV By transforming the non-convex
optimization problem to a monotonic
optimization problem, the authors find
the optimal UAV placement
[70] Maximizing sum rate of edge users Aerial base station supporting cell-
edge users
Mobile UAV This paper transforms the mixed-integer
nonconvex problem to two convex
problems and proposes an iterative
algorithm to solve them by optimizing
UAV trajectory
[71] Maximizing the minimum average
throughput of all users
Aerial base station Mobile UAV This paper proposes an iterative param-
eter-assisted block coordinate descent
method to optimize the UAV trajectory
and OFDMA resource allocation
alternately
[72] Minimizing the mission completion
time
Disseminate a common file to a set of
ground terminals
Mobile UAV They consider trajectory as connected
line segments, which can be obtained
by finding the optimal set of waypoints
and UAV speeds
[58] Maximizing the average secrecy rate UAV and a ground node communicat-
ing with each other
Mobile UAV Proposes iterative algorithms to solve the
non-convex problems by applying the
block coordinate descent and succes-
sive convex optimization

UAV Placement andTrajectory Design Optimization: ASurvey
1 3
common channel models are introduced. We also introduce a variety of optimization prob-
lem solving methods. Finally, two major issues in the field of UAV placement optimization
are presented.
Funding There is no funding for this publication.
Declarations
Conflict of interest The authors declare that they have no conflict of interest.
Ethical Approval This article does not contain any studies with human participants or animals performed by
any of the authors.
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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
Ahmad Mazaherifar received his B.Sc. in computer engineering from
Jahrom University in 2016 and he is currently pursuing his M.Sc.
degree in information technology—computer networks from Yazd
University. His research interests include UAV communications and
Internet of Things.
Seyedakbar Mostafavi is currently an assistant professor at depart-
ment of computer engineering, Yazd University, Yazd, Iran. He has
completed his B.Sc. in Information Technology at Sharif University of
Technology and his MSc. and PhD. in Computer Networks at Amirka-
bir University of Technology (Tehran Polytechnic). Dr. Mostafavi is
the director of “Information Technology Enterprise Architecture”
research lab at Yazd University. Under his studies, Dr. Mostafavi is
actively involved in research on resource management in cloud com-
puting, Internet of Things and wireless networks. He is also a frequent
reviewer for international journals and conferences.