Joint Communication-Motion Planning for UAV Swarm against Jamming with Multi-Agent Deep Reinforcement Learning

In this paper, we investigate the joint communication-motion planning problem for unmanned aerial vehicle (UAV) swarm in the presence of jammers. Specifically, we consider a cluster-based UAV swarm architecture, where multiple cluster member (CM) UAVs transmit messages to a cluster head (CH) UAV through air-to-air links affected by malicious jammers. Our objective is to maximize the sum uplink rate of the UAV swarm by optimizing the trajectories and the transmit power of all UAVs. To achieve this goal, we formulate a joint multi-UAV trajectories and transmit power optimization problem under speed, transmit power, trajectories and received signal-to-interference-plus-noise ratio (SINR) constraints. In order to solve the problem, we establish a Markov decision process (MDP). For the multi-agent environment and the high-dimensional continuous action space, we adopt a multi-agent twin delayed deep deterministic (MATD3) policy gradient-based algorithm. Simulation results show that the proposed scheme can effectively improve the sum uplink rate of the UAV swarm compared to the baseline schemes.