Power Allocation for Secure NOMA Network Based on Q-Learning

This paper investigates the secrecy performance of a multi-relay non-orthogonal multiple access (NOMA) network. Considering the presence of eavesdroppers, an optimal relay selection scheme and a jamming signal transmission scheme for both amplify-and-forward (AF) and decode-and-forward (DF) strategies are proposed. However, the resource allocation problem aimed at maximizing the effective secure throughput (EST) of the system is non-convex. It is difficult to directly solve this optimization problem using conventional methods. As such, a Q-learning approach to solve the resource allocation problem in this system is applied, and an innovative reward function that can maximize the communication quality of edge users while ensuring secure communication of nearby users is designed. According to the analysis of the simulation results, the convergence of the proposed scheme is verified. Under the same conditions, DF relays achieve a higher signal-to-noise ratio (SINR) at the user terminal, and the EST is closely related to the transmission power of the source node and the relays. The numerical results also show that compared to conventional power allocation methods, the proposed method achieves a larger average EST and provides better confidentiality performance.