  The reconfigurable intelligent surface (RIS) is a promising technology for
next-generation wireless communication. It comprises many passive antennas,
which reflect signals from the transmitter to the receiver with adjusted phases
without changing the amplitude. The large number of the antennas enables a huge
potential of signal processing despite the simple functionality of a single
antenna. However, it also makes the RIS configuration a high dimensional
problem, which might not have a closed-form solution and has a high complexity
and, as a result, severe difficulty in online real-time application if we apply
iterative numerical solutions. In this paper, we introduce a machine learning
approach to maximize the weighted sum-rate (WSR). We propose a dedicated neural
network architecture called RISNet. The RIS optimization is designed according
to the RIS property of product and direct channel and homogeneous RIS antennas.
The architecture is scalable due to the fact that the number of trainable
parameters is independent from the number of RIS antennas (because all antennas
share the same parameters). The weighted minimum mean squared error (WMMSE)
precoding is applied and an alternating optimization (AO) training procedure is
designed. Testing results show that the proposed approach outperforms the
state-of-the-art block coordinate descent (BCD) algorithm. Moreover, although
the training takes several hours, online testing with trained model
(application) is almost instant, which makes it feasible for real-time
application. Compared to it, the BCD algorithm requires much more convergence
time. Therefore, the proposed method outperforms the state-of-the-art algorithm
in both performance and complexity.
