Towards reinforcement learning in UAV relay for anti-jamming maritime communications
Chuhuan Liu, Yi Zhang, Guohang Niu, Luliang Jia, Liang Xiao, Jiangxia Luan
Abstract
Maritime communications with sea surface reflections and sea wave occlusions are susceptible to jamming attacks due to the wide geographical area and intensive wireless communication services. Unmanned Aerial Vehicles (UAVs) help relay messages to improve communication performance, but the relay policy that depends on the rapidly changing maritime environments is difficult to optimize. In this paper, a reinforcement learning-based UAV relay policy for maritime communications is proposed to resist jamming attacks. Based on previous transmission performance, the relay location, the received power of the transmitted signal and the received jamming power, this scheme optimizes the UAV trajectory and relay power to save the energy consumption and decrease the Bit-Error-Rate (BER) of the maritime signals. A deep reinforcement learning-based scheme is also proposed, which designs a deep neural network with dueling architecture to further improve the communication performance and computational complexity. The performance bounds regarding the signal to interference plus noise ratio, energy consumption and the communication utility are provided based on the Nash equilibrium of the game against jamming, and the computational complexity of the proposed schemes is analyzed. Simulation results show that the proposed schemes improve the energy efficiency and decrease the BER compared with the benchmark.