Multi-UAV IRS-Assisted Communications: Multinode Channel Modeling and Fair Sum-Rate Optimization via Deep Reinforcement Learning

Unmanned aerial vehicles (UAVs) combined with intelligent reflective surfaces (IRSs) represent a cutting-edge technology for improving the channel capacity of wireless communications, by capitalizing on UAVs’ 3-D mobility coupled with the IRSs’ smart radio capabilities. This work envisions a scenario in which a swarm of UAVs equipped with IRSs serves multiple Internet of Things (IoT) ground nodes (GNs) concurrently transmitting to a single base station (BS) via OFDMA. The huge number of passive elements composing the IRSs introduces a significant complexity in the mission design. Therefore, each IRS is divided into patches that can be simultaneously used to serve different nodes. Considering general Rician fading, a comprehensive channel model for IRS-assisted UAV-aided networks is derived. Then, a multiobjective mixed-integer nonlinear programming problem is conceived to maximize the sum-rate of the GNs and, at the same time, minimize the difference among the users’ data rates, by jointly optimizing the trajectories and the phase shift matrices. This nonconvex problem, reformulated in terms of scheduling (i.e., patch-GN assignment), is challenging to solve. Hence, it is rearranged as a Markov Decision Process and a quasi-optimal solution is obtained via Deep Reinforcement Learning. Extensive simulation analysis is performed to validate the results and the accuracy of the proposed model.