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This paper considers an uplink reconfigurable intelligent surface (RIS)-aidedmassive multiple-input multiple-output (MIMO) system with statistical channelstate information (CSI). The RIS is deployed to help conventional massive MIMOnetworks serve the users in the dead zone. We consider the Rician channel modeland exploit the long-time statistical CSI to design the phase shifts of theRIS, while the maximum ratio combination (MRC) technique is applied for theactive beamforming at the base station (BS) relying on the instantaneous CSI.Firstly, we reveal the power scaling laws and derive the closed-formexpressions for the uplink achievable rate which holds for arbitrary numbers ofbase station (BS) antennas. Based on the theoretical expressions, we discussthe rate performance under some special cases and provide the averageasymptotic rate when using random phase shifts. Then, we consider the sum-ratemaximization and the minimum user rate maximization problems by optimizing thephase shifts at the RIS. However, these two optimization problems arechallenging to solve due to the complicated data rate expression. To solvethese problems, we propose a novel genetic algorithm (GA) with low complexitybut can achieve considerable performance. Finally, extensive simulations areprovided to validate the benefits by integrating RIS into conventional massiveMIMO systems. Besides, our simulations demonstrate the feasibility of deployinglarge-size but low-resolution RIS in massive MIMO systems.

Power Scaling Law Analysis and Phase Shift Optimization of RIS-aided Massive MIMO Systems with Statistical CSI