Joint Trajectory Planning, Time and Power Allocation to Maximize Throughput in UAV Network
Author:
Wang Kehao1ORCID, Xu Jiangwei1ORCID, Li Xiaobai2, Liu Pei13ORCID, Cao Hui1, Liu Kezhong4
Affiliation:
1. School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China 2. Air Force Early Warning Academy, Wuhan 430019, China 3. Integrated Computing and Chip Security Sichuan Collaborative Innovation Center, Chengdu University of Information Technology, Chengdu 610059, China 4. School of Navigation, Wuhan University of Technology, Wuhan 430070, China
Abstract
Due to the advantages of strong mobility, flexible deployment, and low cost, unmanned aerial vehicles (UAVs) are widely used in various industries. As a flying relay, UAVs can establish line-of-sight (LOS) links for different scenarios, effectively improving communication quality. In this paper, considering the limited energy budget of UAVs and the existence of multiple jammers, we introduce a simultaneous wireless information and power transfer (SWIPT) technology and study the problems of joint-trajectory planning, time, and power allocation to increase communication performance. Specifically, the network includes multiple UAVs, source nodes (SNs), destination nodes (DNs), and jammers. We assume that the UAVs need to communicate with DNs, the SNs use the SWIPT technology to transmit wireless energy and information to UAVs, and the jammers can interfere with the channel from UAVs to DNs. In this network, our target was to maximize the throughput of DNs by optimizing the UAV’s trajectory, time, and power allocation under the constraints of jammers and the actual motion of UAVs (including UAV energy budget, maximum speed, and anti-collision constraints). Since the formulated problem was non-convex and difficult to solve directly, we first decomposed the original problem into three subproblems. We then solved the subproblems by applying a successive convex optimization technology and a slack variables method. Finally, an efficient joint optimization algorithm was proposed to obtain a sub-optimal solution by using a block coordinate descent method. Simulation results indicated that the proposed algorithm has better performance than the four baseline schemes.
Funder
National Natural Science Foundation of China Natural Science Foundation of Hunan Province National Key Research and Development Program of China Integrated Computing and Chip Security Sichuan Collaborative Innovation Center of Chengdu University of Information Technology
Subject
Artificial Intelligence,Computer Science Applications,Aerospace Engineering,Information Systems,Control and Systems Engineering
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