System Cost Function Optimization-Based Data Scheduling and Flight Trajectory for Multi-Antenna UAV-Assisted Communication and Sensing Integration Systems

作     者：Chai, Rong Wang, Bingyan Sun, Ruijin Jing, Xiaorong Chen, Qianbin 

作者机构：Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Chongqing Key Laboratory of Mobile Communication Technology Chongqing China Xidian University School of Telecommunications Engineering The State Key Laboratory of Integrated Services Networks Xi'an710126 China 

出 版 物：《IEEE Transactions on Cognitive Communications and Networking》 (IEEE Trans. Cogn. Commun. Netw.)

年 卷 期：2024年

核心收录：

学科分类：0710[理学-生物学] 0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0835[工学-软件工程] 0714[理学-统计学（可授理学、经济学学位）] 0825[工学-航空宇航科学与技术] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Cost functions 

摘      要：In this paper, we consider a multi-antenna unmanned aerial vehicle (UAV)-assisted integrated communication and sensing system, and investigate the joint data scheduling and UAV trajectory optimization problem where multi-antenna UAVs are capable of receiving data packets from users and detecting the condition of targets. Stressing the importance of user communication performance and the energy consumption of the UAVs, we define system cost function as a weighted function of system energy consumption and user transmission rate, and formulate the communication and sensing scheduling, sensing precoding design and UAV flight trajectory optimization problem as a constrained system cost function optimization problem. Given that the optimization problem we have formulated is non-convex and complex to address directly, we break it down into three distinct subproblems, i.e., UAV flight trajectory optimization subproblem, communication and sensing scheduling subproblem, and sensing precoding subproblem, propose an iterative nested method to solve these subproblems. To tackle UAV flight trajectory optimization subproblem, we model a Markov decision process (MDP) and propose a double deep Q-network (DDQN)-based UAV trajectory optimization algorithm. Given the state of the MDP model, the communication and sensing scheduling subproblem is solved by using Lagrange dual transformation and quadratic transformation methods, and the sensing precoding subproblem is tackled through introducing auxiliary variables and applying equivalent transformation method. Based on the obtained strategy of the subproblems, the reward function of the MDP model is updated and the UAV flight trajectory is determined. The simulation results validate the effectiveness of the proposed algorithm. © 2024 IEEE.