  Multiple access techniques are cornerstones of wireless communications. Their
performance depends on the channel properties, which can be improved by
reconfigurable intelligent surfaces (RISs). In this work, we jointly optimize
MA precoding at the base station (BS) and RIS configuration. We tackle
difficulties of mutual coupling between RIS elements, scalability to more than
1000 RIS elements, and channel estimation. We first derive an RIS-assisted
channel model considering mutual coupling, then propose an unsupervised machine
learning (ML) approach to optimize the RIS. In particular, we design a
dedicated neural network (NN) architecture RISnet with good scalability and
desired symmetry. Moreover, we combine ML-enabled RIS configuration and
analytical precoding at BS since there exist analytical precoding schemes.
Furthermore, we propose another variant of RISnet, which requires the channel
state information (CSI) of a small portion of RIS elements (in this work, 16
out of 1296 elements) if the channel comprises a few specular propagation
paths. More generally, this work is an early contribution to combine ML
technique and domain knowledge in communication for NN architecture design.
Compared to generic ML, the problem-specific ML can achieve higher performance,
lower complexity and symmetry.
