This paper applies the reinforcement learning in the joint relay selection and power allocation in the secure cognitive radio (CR) relay network, where the data buffers and full-duplex jamming are applied at the relay nodes. Two cases are considered: maximizing the throughput with the delay and secrecy constraints, and maximizing the secrecy rate with the delay constraint, respectively. In both cases, the optimization relies on the buffer states, the interference to/from the primary user, and the constraints on the delay and/or secrecy. This makes it mathematically intractable to apply the traditional optimization methods. In this paper, the double deep Q-network (DDQN) is used to solve the above two optimization problems. We also apply the a-priori information in the CR network to improve the DDQN learning convergence. Simulation results show that the proposed scheme outperforms the traditional algorithm significantly.

中文翻译：

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具有双深度 Q 网络的安全认知网络的联合缓冲区辅助混合双工中继选择和功率分配

本文将强化学习应用于安全认知无线电 (CR) 中继网络中的联合中继选择和功率分配，其中数据缓冲区和全双工干扰应用于中继节点。考虑两种情况：分别在延迟和保密约束下最大化吞吐量，以及在延迟约束下最大化保密率。在这两种情况下，优化都依赖于缓冲区状态、对主用户的干扰以及对延迟和/或保密的限制。这使得应用传统的优化方法在数学上变得难以处理。本文采用双深度Q网络（DDQN）来解决上述两个优化问题。我们还在 CR 网络中应用了先验信息来提高 DDQN 学习收敛。