Joint User Association, Interference Cancellation and Power Control for Multi-IRS Assisted UAV Communications
This work addresses resource allocation for multi-IRS assisted UAV communications, which is incremental as it extends single-IRS studies to multiple IRSs.
The paper tackles the challenge of joint multi-IRS multi-user association in UAV communications by proposing an optimization algorithm for IRS-user association, UAV trajectory, SIC decoding order, and power allocation to maximize energy efficiency, showing significant advantages in convergence rate and energy efficiency.
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.