Due to the high maneuverability, flexible deployment, and low cost, unmanned aerial vehicles (UAVs) have attracted significant interest recently in assisting wireless communication. This paper considers a multi-UAV enabled wireless communication system, where multiple UAV-mounted aerial base stations are employed to serve a group of users on the ground. To achieve fair performance among users, we maximize the minimum throughput over all ground users in the downlink communication by optimizing the multiuser communication scheduling and association jointly with the UAV’s trajectory and power control. The formulated problem is a mixed integer nonconvex optimization problem that is challenging to solve. As such, we propose an efficient iterative algorithm for solving it by applying the block coordinate descent and successive convex optimization techniques. Specifically, the user scheduling and association, UAV trajectory, and transmit power are alternately optimized in each iteration. In particular, for the nonconvex UAV trajectory and transmit power optimization problems, two approximate convex optimization problems are solved, respectively. We further show that the proposed algorithm is guaranteed to converge. To speed up the algorithm convergence and achieve good throughput, a low-complexity and systematic initialization scheme is also proposed for the UAV trajectory design based on the simple circular trajectory and the circle packing scheme. Extensive simulation results are provided to demonstrate the significant throughput gains of the proposed design as compared to other benchmark schemes.

Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks

The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains, including real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in the UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate more than $45 Billion market value of UAV usage. In this paper, we present UAV civil applications and their challenges. We also discuss the current research trends and provide future insights for potential UAV uses. Furthermore, we present the key challenges for UAV civil applications, including charging challenges, collision avoidance and swarming challenges, and networking and security-related challenges. Based on our review of the recent literature, we discuss open research challenges and draw high-level insights on how these challenges might be approached.

Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges

In this chapter we deal with the following nonlinear fractional programming problem: 

$$P:\mathop{{\max }}\limits_{{x \in s}} q(x) = (f(x) + \alpha )/((x) + \beta )$$

where f, g: R n → R, α, β ∈ R, S ⊆ R n . To simplify things, and without restricting the generality of the problem, it is usually assumed that, g(x) + β + 0 for all x ∈ S,S is non-empty and that the objective function has a finite optimal value.

Nonlinear Fractional Programming

Unmanned aerial vehicles (UAVs) have found numerous applications and are expected to bring fertile business opportunities in the next decade. Among various enabling technologies for UAVs, wireless communication is essential and has drawn significantly growing attention in recent years. Compared to the conventional terrestrial communications, UAVs’ communications face new challenges due to their high altitude above the ground and great flexibility of movement in the 3-D space. Several critical issues arise, including the line-of-sight (LoS) dominant UAV-ground channels and induced strong aerial-terrestrial network interference, the distinct communication quality-of-service (QoS) requirements for UAV control messages versus payload data, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the exploitation of the new design degree of freedom (DoF) brought by the highly controllable 3-D UAV mobility. In this article, we give a tutorial overview of the recent advances in UAV communications to address the above issues, with an emphasis on how to integrate UAVs into the forthcoming fifth-generation (5G) and future cellular networks. In particular, we partition our discussion into two promising research and application frameworks of UAV communications, namely UAV-assisted wireless communications and cellular-connected UAVs, where UAVs are integrated into the network as new aerial communication platforms and users, respectively. Furthermore, we point out promising directions for future research.

Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond

UAV-Enabled Aerial Base Station (BS) I/III: Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks

Unmanned aerial vehicles (UAVs) have a great potential for improving the performance of wireless communication systems due to their high mobility. In this correspondence, we study a UAV-enabled data collection system, where a UAV is dispatched to collect a given amount of data from a ground terminal (GT) at fixed location. Intuitively, if the UAV flies closer to the GT, the uplink transmission energy of the GT required to send the target data can be more reduced. However, such UAV movement may consume more propulsion energy of the UAV, which needs to be properly controlled to save its limited on-board energy. As a result, the transmission energy reduction of the GT is generally at the cost of higher propulsion energy consumption of the UAV, which leads to a new fundamental energy tradeoff in ground-to-UAV wireless communication. To characterize this tradeoff, we consider two practical UAV trajectories, namely circular flight and straight flight. In each case, we first derive the energy consumption expressions of the UAV and GT and then find the optimal GT transmit power and UAV trajectory that achieve different Pareto optimal tradeoffs between them. Numerical results are provided to corroborate our study.

Energy Tradeoff in Ground-to-UAV Communication via Trajectory Design

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent concept, where a UAV equipped with an MEC server is deployed to serve a number of terminal devices (TDs) of Internet of Things in a finite period. In this article, each TD has a certain latency-critical computation task in each time slot to complete. Three computation strategies can be available to each TD. First, each TD can operate local computing by itself. Second, each TD can partially offload task bits to the UAV for computing. Third, each TD can choose to offload task bits to access point via UAV relaying. We propose a new optimization problem formulation that aims to minimize the total energy consumption including communication-related energy, computation-related energy and UAV’s flight energy by optimizing the bits allocation, time slot scheduling, and power allocation as well as UAV trajectory design. As the formulated problem is nonconvex and difficult to find the optimal solution, we propose to solve the problem by two parts, and obtain the near optimal solution by the Lagrangian duality method and successive convex approximation technique, respectively. By analysis, the proposed algorithm can be guaranteed to converge within a dozen of iterations. Finally, numerical results are given to validate the proposed algorithm, which is verified to be efficient and superior to the other benchmark cases.

/pdf/joint-computation-and-communication-design-for-uav-assisted-1dbpnbps8s.pdf

Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT

Unmanned aerial vehicle (UAV) has been widely applied in internet-of-things (IoT) scenarios while the security for UAV communications remains a challenging problem due to the broadcast nature of the line-of-sight (LoS) wireless channels. This article investigates the security problems for dual UAV-assisted mobile edge computing (MEC) systems, where one UAV is invoked to help the ground terminal devices (TDs) to compute the offloaded tasks and the other one acts as a jammer to suppress the vicious eavesdroppers. In our framework, minimum secure computing capacity maximization problems are proposed for both the time division multiple access (TDMA) scheme and non-orthogonal multiple access (NOMA) scheme by jointly optimizing the communication resources, computation resources, and UAVs’ trajectories. The formulated problems are non-trivial and challenging to be solved due to the highly coupled variables. To tackle these problems, we first transform them into more tractable ones then a block coordinate descent based algorithm and a penalized block coordinate descent based algorithm are proposed to solve the problems for TDMA and NOMA schemes, respectively. Finally, numerical results show that the security computing capacity performance of the systems is enhanced by the proposed algorithms as compared with the benchmarks. Meanwhile, the NOMA scheme is superior to the TDMA scheme for security improvement.

Joint Resource and Trajectory Optimization for Security in UAV-Assisted MEC Systems

This paper investigates the secrecy energy efficiency (SEE) maximization problem for unmanned aerial vehicle (UAV) enabled mobile relaying system, where a high-mobility UAV is exploited to assist transmitting confidential information from a ground source to a legitimate ground destination, in the presence of a potential eavesdropper. We aim to maximize the SEE of the UAV by jointly optimizing the communication scheduling, power allocation, and UAV trajectory over a given time horizon. The formulated problem is non-convex that is challenging to be optimally solved. To make the problem more tractable, we decompose the problem into three subproblems, and propose an efficient iterative algorithm that alternately optimizes each block of the variables with the others fixed. Moreover, the practical scenario with uncertain eavesdropper location is investigated to evaluate the performance of the proposed solution. Double circular flight and running track shape flight cases are considered to drawn more insights. Simulation results show that the proposed design significantly improves the SEE of the UAV, as compared to the benchmark schemes.

Secrecy Energy Efficiency Maximization for UAV-Enabled Mobile Relaying

Unmanned aerial vehicles (UAVs) have a great potential for improving the performance of wireless communication systems due to their wide coverage and high mobility. In this paper, we study a UAV-enabled data collection system, where a UAV is dispatched to collect a given amount of data from a ground terminals (GT) at fixed location. Intuitively, if the UAV flies closer to the GT, the uplink transmission energy of the GT required to send the target data can be more reduced. However, such UAV movement may consume more propulsion energy of the UAV, which needs to be properly controlled to save its limited on-board energy. As a result, the transmission energy reduction of the GT is generally at the cost of higher propulsion energy consumption of the UAV, which leads to a new fundamental energy trade-off in Ground-to-UAV (G2U) wireless communication. To characterize this trade-off, we consider two practical UAV trajectories, namely circular flight and straight flight. In each case, we first derive the energy consumption expressions of the UAV and GT, and then find the optimal GT transmit power and UAV trajectory that achieve different Pareto optimal trade-off between them. Numerical results are provided to corroborate our study.

Dingcheng Yang

Papers

Energy Tradeoff in Ground-to-UAV Communication via Trajectory Design

Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT

Joint Resource and Trajectory Optimization for Security in UAV-Assisted MEC Systems

Secrecy Energy Efficiency Maximization for UAV-Enabled Mobile Relaying

Energy Trade-off in Ground-to-UAV Communication via Trajectory Design