A Joint UAV Trajectory, User Association, and Beamforming Design Strategy for Multi-UAV-Assisted ISAC Systems

被引:1
|
作者
Zhang, Ruizhi [1 ]
Zhang, Ying [1 ]
Tang, Rui [2 ,3 ]
Zhao, Huapeng [4 ]
Xiao, Qing [1 ]
Wang, Chenye [1 ]
机构
[1] Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Peoples R China
[2] China West Normal Univ, Sch Elect Informat Engn, Key Lab Optimizat Theory & Applicat, Nanchong 637002, Peoples R China
[3] China West Normal Univ, Internet Things Percept & Big Data Anal Key Lab N, Nanchong 637002, Peoples R China
[4] Univ Elect Sci & Technol China, Sch Elect Sci & Engn, Chengdu 611731, Peoples R China
来源
IEEE INTERNET OF THINGS JOURNAL | 2024年 / 11卷 / 18期
基金
中国国家自然科学基金;
关键词
Alternating optimization; fractional programming (FP); integrated sensing and communication (ISAC); matching theory; resource allocation; sequential quadratic programming (SQP); unmanned aerial vehicle (UAV); DATA-COLLECTION; POWER-CONTROL; COMMUNICATION; RADAR; OPTIMIZATION;
D O I
10.1109/JIOT.2024.3430390
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this article, we investigate a resource allocation problem for a multiunmanned aerial vehicle (UAV) assisted integrated sensing and communication (ISAC) system, where a group of dual-functional UAVs perform simultaneous radar sensing of a target and data communication with multiple ground users (GUs). In particular, the trajectory of UAVs, user association, and beamforming design are jointly considered to maximize the sum weighted bit rate of all GUs while ensuring the sensing beampattern gain of the target. To cope with the above mixed-integer nonconvex optimization problem, we propose an efficient strategy by decomposing the original problem into two subproblems under the alternating optimization framework. For the user association and beamforming design, we propose a novel algorithm to circumvent the coupling relationship among GUs and UAVs by leveraging matching theory and fractional programming theory. For the nonconvex UAV trajectory subproblem, we apply the sequential quadratic programming to obtain a suboptimal solution by solving a sequence of quadratic programming problems. The above two subproblems are iteratively solved and a stable solution is obtained upon convergence. Simulation results show that the proposed strategy outperforms various benchmark schemes that are based on the deferred acceptance algorithm, K-means algorithm, and a heuristic algorithm. It is demonstrated that the proposed strategy efficiently improve the sensing beampattern gain and communication rate.
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页码:29360 / 29374
页数:15
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