Sensing-Error-Aware UAV Scheduling Based on Generative Diffusion-Driven MADRL for ISAC-Enabled Multi-UAV Systems

doi:10.1109/TWC.2025.3638787

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	Sensing-Error-Aware UAV Scheduling Based on Generative Diffusion-Driven MADRL for ISAC-Enabled Multi-UAV Systems
	Wu, Yihao 1,2,3; Yu, Hanxiao 1,2,3; Zhou, Yiqing 1,2,3; Shi, Ningzhe 1,2,3; Cai, Qing 1,2,3; Shi, Jinglin 1,2,3
	2026
发表期刊	IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
ISSN	1536-1276
卷号	25 页码:9782-9798
摘要	In integrated sensing and communication (ISAC) enabled uncrewed aerial vehicle (UAV) systems, based on sensed information such as user positions, UAV scheduling could be optimized to enhance the communication performance. However, sensing errors are inevitable, leading to a performance degradation. This paper proposes a sensing-error-aware (SEA) multi-UAV scheduling scheme (SEA-scheduling). First, the impact of the sensing errors on communication performance is analyzed, and a SEA communication rate is derived. Then, targeting to maximize this SEA rate, multi-UAV collaborative scheduling is jointly optimized with sensing resource allocation. The problem is solved by decomposing into two subproblems, i.e., a joint UAV position schedule, user association and bandwidth allocation optimization subproblem (PUB) and a sensing resource optimization subproblem (SRO), which can be solved iteratively. A generative diffusion(GD)-driven multi-agent reinforcement learning (GD-MADRL) algorithm is proposed to solve PUB, and a classical simulated annealing (SA) algorithm is adopted to solve SRO. The main idea of GD-MADRL is to introduce the GD model in MADRL to generate training data with sensing errors, enhancing the robustness of generated UAV scheduling strategies. Simulation results demonstrate that when there are sensing errors, the proposed SEA-scheduling scheme improves the communication rate by up to 30% compared to existing sensing-error-unaware schemes.
关键词	Sensors Autonomous aerial vehicles Optimization Resource management Integrated sensing and communication Wireless communication Heuristic algorithms Trajectory Diffusion models Wireless sensor networks Generative diffusion model integrated sensing and communication sensing error multi-agent reinforcement learning uncrewed aerial vehicle
DOI	10.1109/TWC.2025.3638787
收录类别	SCI
语种	英语
WOS研究方向	Engineering ; Telecommunications
WOS类目	Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:001659566900033
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42908
专题	中国科学院计算技术研究所
通讯作者	Zhou, Yiqing
作者单位	1.Chinese Acad Sci, Inst Comp Technol, State Key Lab Proc, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wu, Yihao,Yu, Hanxiao,Zhou, Yiqing,et al. Sensing-Error-Aware UAV Scheduling Based on Generative Diffusion-Driven MADRL for ISAC-Enabled Multi-UAV Systems[J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS,2026,25:9782-9798.
APA	Wu, Yihao,Yu, Hanxiao,Zhou, Yiqing,Shi, Ningzhe,Cai, Qing,&Shi, Jinglin.(2026).Sensing-Error-Aware UAV Scheduling Based on Generative Diffusion-Driven MADRL for ISAC-Enabled Multi-UAV Systems.IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS,25,9782-9798.
MLA	Wu, Yihao,et al."Sensing-Error-Aware UAV Scheduling Based on Generative Diffusion-Driven MADRL for ISAC-Enabled Multi-UAV Systems".IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 25(2026):9782-9798.