Analysis and Optimization of RIS-aided Massive MIMO Systems with Statistical CSI

This paper considers an uplink reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) system with statistical channel state information (CSI). The RIS is deployed to help conventional massive MIMO networks serve the users in the dead zone. We consider the Rician channel model and exploit the long-time statistical CSI to design the phase shifts of the RIS, while the maximum ratio combination (MRC) technique is applied for the active beamforming at the base station (BS) relying on the instantaneous CSI. Firstly, we derive the closed-form expressions for the uplink achievable rate which holds for arbitrary numbers of base station (BS) antennas. Then, we propose a genetic algorithm (GA)-based method to maximize the minimum user rate by optimizing the phase shifts at the RIS. Finally, extensive simulations are provided to validate the benefits by integrating RIS into conventional massive MIMO systems.