Dynamic User Clustering and Backscatter-Enabled RIS-Assisted NOMA ISAC

In this study, we investigate the performance of a hybrid reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) network, augmented with backscattering capabilities, designed to facilitate integrated sensing and communication (ISAC). Our primary objective is two-fold: first, to enhance the overall communication throughput, and second, to strengthen the sensing power for target detection. To achieve these goals, we introduce two novel dynamic user clustering algorithms namely composite distance and angle-based (CDA) and channel-oriented adaptive (COA) algorithm for grouping users into clusters with fixed base station and RIS positions, where the successive interference cancellation (SIC) is employed for effective communication within each pair. Moreover, we present a comprehensive optimization problem that jointly maximizes the sum rate and sensing power. This problem involves optimizing the transmit beamformer at the base station, the power allocation factors within each cluster, and the phase shifts at the RIS. This methodology not only adheres to strict power constraints and quality of service requirements at each receiving node but also ensures equitable resource allocation among the targets and enforces unit modulus phase shifts at each RIS element. To tackle the complex interdependencies and non-convex nature of the optimization problem, we introduce an advanced iterative algorithm based on alternative optimization (AO). This state-of-the-art technique employs successive convex approximation (SCA) to systematically address this multifaceted problem. Finally, the simulation results empirically validate the proposed algorithm's effectiveness, considering the number of RIS elements, maximum power budget, number of targets, and imperfect channel state information (CSI) while showing the trade-off between communication and sensing performance.