Adaptive Communication for Joint Trajectory and RRM in MADRL-Based UAV Networks

This paper addresses the joint design of Unmanned Aerial Vehicles (UAVs) trajectory and radio resource management (RRM) in dynamic wireless environments by leveraging a multi-agent deep reinforcement learning (MADRL) framework. In contrast to prior works that either assume constant synchronization between agents and the controller or overlook the communication cost, we explicitly model the interaction between UAVs and the central controller. We propose an adaptive synchronization strategy that selectively transmits model parameters and experience data based on their relevance, enabling a resource-aware RRM algorithm that optimally balances learning performance and communication overhead. The MADRL agents optimize their trajectories based on rewards that incorporate priorities derived from the RRM layer, which jointly manages both uplink and downlink communications. Simulation results demonstrate that our event-driven synchronization strategy outperforms periodic baselines in both convergence speed and communication overhead, towards scalable deployment in realistic urban environments.