Opportunistic Scheduling Scheme to Improve Physical-Layer Security in Cooperative NOMA System: Performance Analysis and Deep Learning Design

In this paper, we propose a novel opportunistic scheduling-based antenna-user selection (OBAUS) scheme to improve the secrecy performance of cooperative non-orthogonal multiple access (NOMA) systems in the presence of a passive eavesdropper. The proposed OBAUS scheme can be divided into two steps such as relay antenna selection which selects the antenna at the relay to minimize the eavesdropper channel condition, and user selection which selects the received antenna at each user to maximize the main channel gains, respectively. To capture the relation between network parameters and secrecy performance, we derive the closed-form expression for secrecy outage probability (SOP) of cell-center and cell-edge users, respectively. Toward a real-time setting, we design the deep neural network (DNN)-based optimization that predicts each user channel capacity, the optimal values of transmit power, and the power allocation coefficient at the same time. Numerical results show that the proposed scheme can improve the SOP and secrecy throughput performances compared to that of the conventional scheduling scheme, called the random relay-user pair antenna selection (RRUAS) scheme. Besides that, the proposed DNN-based optimization can predict the secrecy performance and find the optimal points. Compared to that of the traditional searching algorithm (2D Golden Section Searching), the proposed DNN-based optimization significantly reduces the execution time as well as reasonable secrecy performance prediction and optimal point finding. Moreover, the impacts of essential network parameters such as transmit power at source and relay, the number of relay's and user's antennas, power allocation coefficient, and minimum secrecy data rates on the system secrecy performance are investigated to show the effectiveness of the proposed scheme.