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Joint Power Allocation and Trajectory Design for UAV-enabled Secure Communication System

In this work, we consider a downlink UAV-enabled secure communication system composed of two unmanned aerial vehicles (UAVs) and multiple ground users, in which one UAV works as the communication base station and the other transmits the jamming signals to restrict the suspicious eavesdroppers near the ground users. To guarantee the secrecy in communication between UAVs and ground users, we formulate an average secrecy rate maximization problem. Firstly, we decompose the non-convex problem into trajectory design and subcarrier power allocation subproblems. Then, in order to solve this non-convex problem, an intelligent multi-agent-based deep Q-network (DQN) method is proposed to optimize the corresponding transmit power and trajectory of UAVs jointly. The experience replay method in the DQN can not only enable the agent to quickly adjust to the dynamic environment through the stored experience but also can train its neural network. Simulation results show that the average secrecy rate of the proposed scheme increases by 10.29% in comparison to the benchmark scheme.