  The reconfigurable intelligent surface (RIS) is a promising technology that
enables wireless communication systems to achieve improved performance by
intelligently manipulating wireless channels. In this paper, we consider the
sum-rate maximization problem in a downlink multi-user
multi-input-single-output (MISO) channel via space-division multiple access
(SDMA). Two major challenges of this problem are the high dimensionality due to
the large number of RIS elements and the difficulty to obtain the full channel
state information (CSI), which is assumed known in many algorithms proposed in
the literature. Instead, we propose a hybrid machine learning approach using
the weighted minimum mean squared error (WMMSE) precoder at the base station
(BS) and a dedicated neural network (NN) architecture, RISnet, for RIS
configuration. The RISnet has a good scalability to optimize 1296 RIS elements
and requires partial CSI of only 16 RIS elements as input. We show it achieves
a high performance with low requirement for channel estimation for geometric
channel models obtained with ray-tracing simulation. The unsupervised learning
lets the RISnet find an optimized RIS configuration by itself. Numerical
results show that a trained model configures the RIS with low computational
effort, considerably outperforms the baselines, and can work with discrete
phase shifts.
