Multiple Access for Holographic Reconfigurable Intelligent Surface (HRIS)-aided Near-Field Communications

This work investigates the performance of rate splitting multiple access (RSMA) in a holographic reconfigurable intelligent surface (HRIS)-aided downlink network for efficient near-field communication. We formulate a sum-rate maximization problem that jointly optimizes the transmit beamforming at the base station (BS), the common rate of each receiving internet of things (IoT) node, and beamforming at the HRIS transmission design to ensure a minimum quality of service (QoS) at each node under the available resource constraints, such as the total power budget at the BS. Since the optimization problem is non-convex due to the coupling of the variables, we propose an iterative algorithm based on alternating optimization (AO) that efficiently solves the joint optimization problem utilizing analytical tools such as the successive convex approximation (SCA). Various numerical results are shown to validate the effectiveness and convergence of the proposed algorithm. Furthermore, we also discuss the impact of the key system parameters, such as reflecting elements, minimum QoS constraint, transmit power budget, and number of IoT nodes. The dominance of RSMA over the counterpart, non-orthogonal multiple access (NOMA), is also demonstrated. It is shown that the use of RSMA can achieve up to 96% higher performance compared to NOMA. It is also highlighted that with near-field assumptions, the average sum rate increases around 69% compared to hybrid 66% and far-field model 64%.