Deep learning–based resource allocation for secure transmission in a non-orthogonal multiple access network

Abstract

Machine learning techniques, especially deep learning algorithms have been widely utilized to deal with different kinds of research problems in wireless communications. In this article, we investigate the secrecy rate maximization problem in a non-orthogonal multiple access network based on deep learning approach. In this non-orthogonal multiple access network, the base station intends to transmit two integrated information: a confidential information to user 1 (the strong user) and a broadcast information to user 1 and user 2. In addition, there exists an eavesdropper that intends to decode the confidential information due to the broadcast nature of radio waves. Hence, we formulate the optimization problem as a secrecy rate maximization problem. We first solve this problem by employing convex optimization technique, then we generate the training, validation, and test dataset. We propose a deep neural network–based approach to learn to optimize the resource allocations. The advantages of the proposed deep neural network are the capabilities to achieve low complexity and latency resource allocations. Simulation results are provided to show that the proposed deep neural network approach is capable of reaching near-optimal secrecy rate performance with significantly reduced computational time, when compared with the benchmark conventional approach.