Deep Q-Network Based Dynamic Trajectory Design for UAV-Aided Emergency Communications

作     者：Liang Wang Kezhi Wang Cunhua Pan Xiaomin Chen Nauman Aslam Liang Wang;Kezhi Wang;Cunhua Pan;Xiaomin Chen;Nauman Aslam

作者机构：Department of Computer and Information ScienceNorthumbria UniversityNewcastle upon TyneNE18STU.K School of Electronic Engineering and Computer ScienceQueen Mary University of LondonLondonE14NSU.K 

出 版 物：《Journal of Communications and Information Networks》 (通信与信息网络学报（英文）)

年 卷 期：2020年第5卷第4期

页      面：393-402页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 082503[工学-航空宇航制造工程] 081001[工学-通信与信息系统] 0825[工学-航空宇航科学与技术] 

基　　金：The associate editor coordinating the review of this paper and approving it for publication was J.Zhang 

主　　题：deep reinforcement learning deep Q-network(DQN) successive convex approximation(SCA) UAV power control 

摘      要：In this paper,an unmanned aerial vehicle(UAV)-aided wireless emergence communication system is studied,where a UAV is deployed to support ground user equipments(UEs)for emergence *** aim to maximize the number of the UEs served,the fairness,and the overall uplink data rate via optimizing the trajectory of UAV and the transmission power of *** propose a deep Q-network(DQN)based algorithm,which involves the well-known deep neural network(DNN)and Q-learning,to solve the UAV trajectory ***,based on the optimized UAV trajectory,we further propose a successive convex approximation(SCA)based algorithm to tackle the power control problem for each *** simulations demonstrate that the proposed DQN based algorithm can achieve considerable performance gain over the existing benchmark algorithms in terms of fairness,the number of UEs served and overall uplink data rate via optimizing UAV’s trajectory and power optimization.