  Reconfigurable intelligent surface (RIS) technology has recently emerged as a
spectral- and cost-efficient approach for wireless communications systems.
However, existing hand-engineered schemes for passive beamforming design and
optimization of RIS, such as the alternating optimization (AO) approaches,
require a high computational complexity, especially for
multiple-input-multiple-output (MIMO) systems. To overcome this challenge, we
propose a low-complexity unsupervised learning scheme, referred to as
learning-phase-shift neural network (LPSNet), to efficiently find the solution
to the spectral efficiency maximization problem in RIS-aided MIMO systems. In
particular, the proposed LPSNet has an optimized input structure and requires a
small number of layers and nodes to produce efficient phase shifts for the RIS.
Simulation results for a 16x2 MIMO system assisted by an RIS with 40 elements
show that the LPSNet achieves 97.25% of the SE provided by the AO counterpart
with more than a 95% reduction in complexity.
