Integrated Relay Selection and Power Allocation for Full-Duplex Relay Networks via Deep Reinforcement Learning

Full-duplex (FD) systems enable users to simultaneously transmit and receive information on the same frequency, theoretically enhancing spectrum efficiency. However, the inherent openness and broadcasting nature of wireless communication pose significant security risks, especially in the presence of eavesdroppers. To enhance the reliability of FD relay systems, we derive the secrecy capacity of the systems and propose a novel approach that integrates relay selection and power allocation utilizing deep reinforcement learning (DRL). Specifically, our method employs deep Q network (DQN) and proximal policy optimization (PPO) algorithms to jointly explore the optimal relay selection and power allocation strategies. Simulation results indicate that the developed approach enhances the average secrecy capacity by at least 30% compared to benchmark methods. Additionally, we assessed the performance of various methods under diverse conditions, demonstrating the effectiveness and adaptability of the proposed approach.