To address the problem of illegals endangering national security through wireless communications,the paper investigates a lawful eavesdropping scheme based on Unmanned Aerial Vehicle(UAV)spoofing relay technology to eavesdrop on the communication links between suspicious nodes on the ground.Firstly,the problem of maximizing the eavesdropping rate is constructed by considering the link between nodes as a line-of-sight link and modeling each channel.Secondly,to solve this complex non-convex optimization problem,the paper adopts a deep reinforcement learning method,comprehensively considers the impact of the three-dimensional trajectory of the UAV,the amplification coefficient,and the power allocation ratio on the eavesdropping rate,and models the problem as a Markov Decision Process,and designs the corresponding reward function.Finally,the joint optimization is implemented using the Twin Delayed Deep Deterministic Policy Gradient(TD3)algorithm.From the numerical results,compared with the active eavesdropping optimization strategy based on Deep Deterministic Policy Gradient algorithm,the optimization strategy based on the TD3 algorithm proposed in this paper has a faster convergence speed,and the performance of eavesdropping is improved.
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