Reconfigurable intelligent surface (RIS) can be crucial in next-generation
communication systems. However, designing the RIS phases according to the
instantaneous channel state information (CSI) can be challenging in practice
due to the short coherent time of the channel. In this regard, we propose a
novel algorithm based on the channel statistics of massive multiple input
multiple output systems rather than the CSI. The beamforming at the base
station (BS), power allocation of the users, and phase shifts at the RIS
elements are optimized to maximize the minimum signal to interference and noise
ratio (SINR), guaranteeing fair operation among various users. In particular,
we design the RIS phases by leveraging the asymptotic deterministic equivalent
of the minimum SINR that depends only on the channel statistics. This
significantly reduces the computational complexity and the amount of
controlling data between the BS and RIS for updating the phases. This setup is
also useful for electromagnetic fields (EMF)-aware systems with constraints on
the maximum user's exposure to EMF. The numerical results show that the
proposed algorithms achieve more than 100% gain in terms of minimum SINR,
compared to a system with random RIS phase shifts, with 40 RIS elements, 20
antennas at the BS and 10 users, respectively