2026 IEEE Wireless Communications and Networking Conference (WCNC) · Conference paper · 2026
Authors: Chuanzi Wang, Chao Dong, Xiaojun Zhu, Jiahao You, Min Zhang
Published in: 2026 IEEE Wireless Communications and Networking Conference (WCNC), Kuala Lumpur, Malaysia, pp. 1–6.
Authorship: Coauthor / 合作作者
DOI: 10.1109/WCNC65185.2026.11555745
Research overview
This paper studies cooperative UAV relaying and user association in heterogeneous low-altitude networks with uncertain demand and mobility.
The problem
Mobile users generate changing transmission demands, while UAVs have limited and heterogeneous resources. Ground or aerial base stations must coordinate with the UAVs when coverage is insufficient.
The approach
The problem jointly optimizes trajectories and dynamic user associations to improve task-completion throughput. Multi-agent hybrid proximal policy optimization handles long-term trajectories, while many-to-one Gale–Shapley matching handles short-term associations.
Evaluation
Simulations compare task completion against benchmark methods under the modeled low-altitude network conditions. The conference paper gives the complete algorithm and evaluation settings.
基于多智能体强化学习的低空异构网络无人机协同传输
本文研究需求与移动性不确定条件下，低空异构网络中的无人机协同中继和用户关联。
研究问题
移动用户产生变化的传输需求，无人机的资源有限且异构。在覆盖不足时，地面或空中基站需要与无人机协同提供服务。
研究方法
论文联合优化轨迹与动态用户关联，以提高任务完成吞吐量。多智能体混合近端策略优化用于长期轨迹规划，多对一 Gale–Shapley 匹配用于短期用户关联。
研究评估
论文在所设低空网络条件下，通过仿真与基线方法比较任务完成情况。完整算法和评估设置见会议原文。
中文题名为便于阅读所作的译名，正式引用请使用英文原题。
Citation
[1] C. Wang, C. Dong, X. Zhu, J. You, and M. Zhang, “UAV cooperative transmission in low-altitude heterogeneous networks based on multi-agent reinforcement learning,” in Proc. IEEE Wireless Commun. Netw. Conf. (WCNC), Kuala Lumpur, Malaysia, 2026, pp. 1–6, doi: 10.1109/WCNC65185.2026.11555745.
