Reinforcement learning-based secure joint relay and jammer selection in dual-hop wireless networks

This paper demonstrates the potential of reinforcement learning techniques for the secure joint relay and jammer selection in the dual-hop wireless cooperative network. The existing techniques solve the optimization problem to find near-optimal secrecy. However, due to the computational complexity, most of these algorithms are not scalable to large networks. In this work, two model-free reinforcement learning-based secure joint relay and jammer selection techniques are developed. The objective is to maximize the physical layer security (PLS) of the dual-hop wireless network. The secure joint relay and jammer selection is transformed into a prediction-based problem and solved using reinforcement learning. We introduced Q-learning-based joint relay and jammer selection (QL-JRJS) and deep Q-learning-based joint relay and jammer selection (DQL-JRJS) algorithms. The secrecy performance of the introduced algorithms is compared with the existing techniques. The simulation results demonstrated that the proposed DQL-JRJS algorithm on average achieves 38.7%, 32.1%, and 21.6% less secrecy outage probability (SOP) as compared to the greedy method (GM), vector alignment method (VAM), and QL-JRJS algorithm. However, QL-JRJS only mimics the performance of DQL-JRJS for small values of state action pairs. The proposed algorithms are best suited for situations where the exact global channel state information (CSI) for all the links is unavailable.