A Novel Spatio-Temporal Rate-Splitting-based Power Allocation Optimization Strategy for RIS-assisted 6G MU-MISO Communication Systems

Reconfigurable intelligent surfaces (RISs) can dynamically adjust phase shifts to achieve scalability in networks, while rate-splitting multiple access (RSMA) can dynamically allocate spectrum and power resources according to the communication needs of IoT devices and reduce the energy consumption of the system. Therefore, the integration of RIS and RSMA can not only enhance the performance of IoT communication systems, but also reduces wastage of limited resources. This paper proposes a novel spatio-temporal rate-splitting-based power allocation optimization strategy for RIS-assisted multi-user (MU) systems to maximize channel capacity and energy efficiency gains. Leveraging the proposed spatio-temporal minimum mean squared error (STMMSE) principle and the rate splitting-based optimal power consumption (RS-OPC) method, the approach considers the Cramér-Rao bound for channel errors and derives expressions for maximizing channel capacity and obtaining the optimal solution. The proposed method selectively adjusts power values for different users and transmission types of symbols to achieve optimal power allocation objectives, thereby ensuring communication quality while optimizing channel capacity. This offers communication systems a higher configurability and resource optimization. Extensive simulation results including channel capacity, energy efficiency (EE) and spectral efficiency (SE) validate the effectiveness of the proposed method.