Multi-UAV IRS-assisted Communications: Multi-Node 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' 3D 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 multi-objective mixed-integer non-linear 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 non-convex 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.