  Reconfigurable intelligent surface (RIS) is an emerging technology for future
wireless communication systems. In this work, we consider downlink spatial
multiplexing enabled by the RIS for weighted sum-rate (WSR) maximization. In
the literature, most solutions use alternating gradient-based optimization,
which has moderate performance, high complexity, and limited scalability. We
propose to apply a fully convolutional network (FCN) to solve this problem,
which was originally designed for semantic segmentation of images. The
rectangular shape of the RIS and the spatial correlation of channels with
adjacent RIS antennas due to the short distance between them encourage us to
apply it for the RIS configuration. We design a set of channel features that
includes both cascaded channels via the RIS and the direct channel. In the base
station (BS), the differentiable minimum mean squared error (MMSE) precoder is
used for pretraining and the weighted minimum mean squared error (WMMSE)
precoder is then applied for fine-tuning, which is nondifferentiable, more
complex, but achieves a better performance. Evaluation results show that the
proposed solution has higher performance and allows for a faster evaluation
than the baselines. Hence it scales better to a large number of antennas,
advancing the RIS one step closer to practical deployment.
