Privacy Transmission via Joint Active and Passive Beamforming Optimization for RIS-Aided NOMA-IoMT Networks

The profound fusion of reconfigurable intelligent surfaces (RIS) with non-orthogonal multiple access (NOMA) holds the potential to substantially boost the data transmission rates within the Internet-of-Medical-Things (IoMT). However, a notable concern arises from the utilization of successive interference cancellation (SIC) technology by NOMA users in the IoMT networks, which raises privacy challenges when dealing with a substantial number of NOMA users within the IoMT ecosystem. This paper introduces a groundbreaking approach leveraging RIS to significantly enhance the security of private users within RIS-aided NOMA networks, specifically tailored for IoMT applications. Our core objective is to maximize the systems secure rate for private users, achieved by a simultaneous optimization of both transmit beamforming and reflecting vectors. Acknowledging the inherent non-convex nature of the optimization problem at hand, we strategically decompose it into two distinct subproblems: the optimization of beamforming strategies and the intricate optimization of RIS reflection mechanisms. Leveraging the established successive convex approximation (SCA) methodology, we adeptly transform these challenging subproblems into tractable and convex forms. Addressing these complex challenges, we introduce a highly efficient algorithm grounded in an alternating optimization paradigm, enabling us to iteratively address the subproblems in a coordinated, synergistic manner. Our simulation results unequivocally showcase substantial gains in secrecy performance afforded by our proposed scheme.