A Novel Alternative Optimization Method for Joint Power and Trajectory Design in UAV-Enabled Wireless Network

Abstract

This letter aims to maximize the average throughput via the joint design of the transmit power and trajectory for unmanned aerial vehicle (UAV)-enabled network. The conventional way to tackle this problem is based on the alternating optimization (AO) method by iteratively updating power and trajectory until convergence, resulting in a non-convex trajectory subproblem which is difficult to deal with. To develop more efficient methods, we propose a novel AO method by incorporating both power and trajectory into an intermediate variable, and then iteratively updating power and the newly introduced variable. This novel variable transformation makes it easier to decompose the original problem into two convex subproblems, namely a throughput maximization subproblem and a feasibility subproblem. Consequently, both of these subproblems can be solved in a globally optimal fashion. We further propose a lowcomplexity algorithm for the feasibility subproblem by exploiting the alternating directional method of multipliers (ADMM), whose updating step is performed in closed-form solutions. Simulation results demonstrate that our proposed method reduces the computation time by orders of magnitude, while achieving higher performance than the conventional methods.Index Terms-Alternative Optimization, ADMM, joint power and trajectory design, unmanned aerial vehicle, throughput maximization.

Joint Mobility, Communication and Computation Optimization for UAVs in Air-Ground Cooperative Networks

“…In both [29], [30], the problem of the coverage and deployment optimization of multiple UAVs has also been studied in terms of energy efficiency, for which the optimal transport and the block coordinate descent methods are applied, respectively. In [14], [23], the authors have investigated the issue on energy-efficient UAV communication and trajectory optimization. In their system designs, the communication throughput in the sense of energy efficiency is treated as the optimization objective.…”

“…In their system designs, the communication throughput in the sense of energy efficiency is treated as the optimization objective. Differently, [14] exploits the sequential convex optimization technique to solve their joint optimization model, while an iterative algorithm has been proposed based on the alternating directional method of multipliers (ADMM) in [23]. Besides focusing on joint communication and trajectory design, many researchers have also been engaged in developing UAV-oriented secure communication methods by jointly considering UAV communication and mobility control such as [25], [31].…”

“…Besides focusing on joint communication and trajectory design, many researchers have also been engaged in developing UAV-oriented secure communication methods by jointly considering UAV communication and mobility control such as [25], [31]. From these aforementioned studies [14], [20], [23]- [26], [28]- [31], it can be summarized that many significant and novel paradigms have been designed and well validated from the perspective of joint UAV communication and mobility optimization. Nevertheless, the decision-making modeling and optimization of mobile edge computing in air-ground cooperative scenarios has been ignored in these works, while the computation-related energy consumption can actually affect the overall energy efficiency of UAVs.…”

Cognitive NOMA for UAV-Enabled Secure Communications: Joint 3D Trajectory Design and Power Allocation

“…Compared to traditional terrestrial communications, UAV-enabled communication has several advantages, such as on-demand deployment, high mobility, and line-of-sight (LoS) dominant UAV-to-ground channels [1], [2]. Therefore, UAV is anticipated to find many potential applications in the future [3]- [6]. However, the UAV system is in the face of spectrum scarcity issue since UAVs usually operate on overloaded unlicensed spectrum bands (e.g., IEEE S-Band, IEEE L-Band, and, Industrial, Scientific, and Medical (ISM) band) [7].…”

“…2) UAV networks UAV communications have been widely studied for wireless networks [3]- [6], [25], [26]. Among them, in [25], the UAV is employed to aid multiple cells for data offloading, where the trajectory is optimized to maximize the sum rate of UAVserved edge users.…”