  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 reveal the power scaling laws and derive the closed-form
expressions for the uplink achievable rate which holds for arbitrary numbers of
base station (BS) antennas. Based on the theoretical expressions, we discuss
the rate performance under some special cases and provide the average
asymptotic rate when using random phase shifts. Then, we consider the sum-rate
maximization and the minimum user rate maximization problems by optimizing the
phase shifts at the RIS. However, these two optimization problems are
challenging to solve due to the complicated data rate expression. To solve
these problems, we propose a novel genetic algorithm (GA) with low complexity
but can achieve considerable performance. Finally, extensive simulations are
provided to validate the benefits by integrating RIS into conventional massive
MIMO systems. Besides, our simulations demonstrate the feasibility of deploying
large-size but low-resolution RIS in massive MIMO systems.
