Optimizing UAV Trajectory and Power Allocation with Deep Reinforcement Learning for Throughput and Fairness in Full-Duplex Networks

This article addresses the resource allocation problem in a multi-unmanned aerial vehicle (UAV)-assisted full-duplex wireless network, where the goal is to optimize UAV trajectory, user association, and power allocation in order to maximize the total uplink and downlink transmission rates for user devices. The proposed optimization framework accounts for the challenges introduced by imperfect self-interference cancellation and incorporates a generalized probabilistic air-ground channel model to increase its practical applicability. However, due to the mobility of UAVs and the dynamic nature of the environment, solving this problem using conventional optimization methods becomes both computationally expensive and impractical. To overcome these challenges, we model the problem as a partially observable stochastic game, where the macro base station (MBS) and UAVs act as agents, each interacting with the environment and receiving distinct observations. Given the complexity of this setting, including large state and action spaces, we adopt the Proximal Policy Optimization (PPO) algorithm. PPO is well-suited to this problem due to its efficiency in handling high-dimensional state-action spaces, its ability to ensure stable learning, and its effectiveness in managing the partial observability of the environment. These features make PPO an ideal approach for solving the resource allocation problem in UAV-assisted full-duplex networks.