Joint Trajectory and Power Design With Cooperative Jamming UAV Assistance Based on Reinforcement Learning

We examine a secure wireless communication system that is enabled by unmanned aerial vehicles (UAVs) in this research. In the wireless communication system with an eavesdropping UAV, we deploy a relay UAV to facilitate the transmission of confidential signals from the source station (denoted as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i>) to ground users. Additionally, we select an idle relay UAV to act as a cooperative jamming UAV, sending interference signals to the eavesdropping UAV. It is quite feasible that the eavesdropping UAV will leverage its mobility to improve the quality of its eavesdropping, making its trajectory unpredictable. First, to address the worst-case scenario for the ground user’s security performance, we assume the eavesdropping UAV approaches at the closest distance.We aim to maximize the worst secrecy rate under perfect CSI via designing the flight trajectory and transmission power of both the relay UAV and the jamming UAV. Second, we investigated the performance of the system’s secrecy outage probability under imperfect CSI. The presence of eavesdropping UAVs and the unpredictable nature of their environment makes traditional convex optimization methods mathematically complex for solving the trajectory optimization problem of the relay and jamming UAVs. To address this, we propose the Multi-Agent joint design trajectory and power (MAJDTP) algorithm based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to optimize the flight trajectory and transmission power of both UAVs. During the design and training process, the relay and jamming UAVs are treated as agents to derive their optimal flight paths and transmission energy. Finally, our approach surpasses the benchmark algorithm, as demonstrated by the simulation results.