Multitask learning-based secure transmission for reconfigurable intelligent surface-aided wireless communications

Abstract

Reconfigurable intelligent surfaces (RISs) represent a highly promising technology that enhances the capacity and coverage of wireless networks by intelligently reconfiguring the wireless propagation environment in highly advanced wireless communications. The objective of this study is to solve the problem of secrecy rate maximization for multiple RIS-aided millimeter-wave communications by jointly optimizing the active RISs and the RIS phase shifts of the considered system. For this nonconvex problem, we propose multitask learning in a deep neural network to predict the RIS phase shift and active RISs. Numerical results based on realistic, three-dimensional, ray-tracing simulations show that the proposed solution can predict the RIS phase and active RIS with an accuracy rate > 96%. These results confirm the viability of RIS-aided secure wireless communications.