  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.
