A Rate-Splitting Strategy for STAR-RIS-Aided Massive MIMO Systems With Joint Optimization

This work proposes a rate-splitting (RS) strategy for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided massive multiple-input multiple-output (mMIMO) systems to reduce the interference among multiple users and enhance the spectral efficiency (SE) while improving the coverage degraded by blockages. Specifically, we use the RS to design the precoder for the common part by solving the asymptotic problem. Also, unlike traditional RIS-aided systems, receivers can be positioned on either side of the RIS panel in the proposed system. We derive the sum-rate based on statistical channel state information (CSI) to reduce the signal overhead. Next, we optimize the rate through a projected gradient ascent method algorithm simultaneously with respect to the amplitudes and phase shifts of the STAR-RIS. Simulations show the advantages of the RS strategy compared with the broadcasting strategy in improving the sum-rate. We further evaluate the efficiency of the STAR-RIS system against the traditional RIS-aided system. In our analysis, we employ energy splitting and mode switching protocols to fine-tune the transmission and reflection coefficients of the outgoing and incoming signals.