Deep Neural Network-Based Precoder for Fairness Aware Secure NOMA Scheme

Abstract

This work proposes an artificial noise (AN)-aided secure multiple-input single-output non-orthogonal multiple access (NOMA) scheme. In the design of the proposed scheme, we consider fairness that all the users have higher secrecy rates as compared to those given by a competing orthogonal multiple access (OMA) scheme. Despite its importance, the fairness aware design has remained rarely touched since it is mathematically intractable. This work shows that this problem can be efficiently solved by utilizing a deep neural network as the precoder for the information and AN signals even without resorting to some assumptions such as a large antenna array and/or a high signal-to-noise ratio. We will also propose an adaptive mode that switches the access protocol from the OMA scheme to the NOMA scheme only when the fairness is met. Performance of the proposed secure NOMA scheme will be extensively evaluated and compared with existing NOMA and OMA schemes. The comparisons clearly show that the sum secrecy rate can be significantly improved while guaranteeing the fairness, which however cannot be achieved with the existing NOMA scheme.