Intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communications are expected to alleviate the load of ground base stations in a cost-effective way. Existing studies mainly focus on the deployment and resource allocation of a single IRS instead of multiple IRSs, whereas it is extremely challenging for joint multi-IRS multi-user association in UAV communications with constrained reflecting resources and dynamic scenarios. To address the aforementioned challenges, we propose a new optimization algorithm for joint IRS-user association, trajectory optimization of UAVs, successive interference cancellation (SIC) decoding order scheduling and power allocation to maximize system energy efficiency. We first propose an inverse soft-Q learning-based algorithm to optimize multi-IRS multi-user association. Then, SCA and Dinkelbach-based algorithm are leveraged to optimize UAV trajectory followed by the optimization of SIC decoding order scheduling and power allocation. Finally, theoretical analysis and performance results show significant advantages of the designed algorithm in convergence rate and energy efficiency.

翻译：智能反射面（IRS）辅助的无人机（UAV）通信有望以经济高效的方式减轻地面基站的负载。现有研究主要关注单个IRS而非多个IRS的部署与资源分配，然而，在反射资源受限且场景动态变化的无人机通信中，实现多IRS与多用户的联合关联极具挑战性。为解决上述挑战，我们提出了一种新的优化算法，用于联合优化IRS-用户关联、无人机轨迹规划、连续干扰消除（SIC）解码顺序调度及功率分配，以最大化系统能量效率。首先，我们提出基于逆软Q学习的算法优化多IRS-多用户关联；其次，利用SCA和Dinkelbach算法优化无人机轨迹，随后优化SIC解码顺序调度与功率分配；最后，理论分析与性能结果表明，所设计算法在收敛速率和能量效率方面具有显著优势。