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

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 .