Abstract
Reconfigurable Intelligent Surfaces (RIS) can enhance system performance at the cost of increased complexity in multiuser MIMO systems. The beamforming options scale with the number of antennas at the base station/RIS. Existing methods for solving this problem tend to use computationally intensive iterative methods that are non-scalable for large RIS-aided MIMO systems. We propose here a novel self-supervised contrastive learning neural network (NN) architecture to optimize the sum spectral efficiency through joint active and passive beamforming design in multiuser RIS-aided MIMO systems. Our scheme utilizes contrastive learning to capture the channel features from augmented channel data and then can be trained to perform beamforming with only 1% of labeled data. The labels are derived through a closed-form optimization algorithm, lever-aging a sequential fractional programming approach. Leveraging the proposed self-supervised design helps to greatly reduce the computational complexity during the training phase. Moreover, our proposed model can operate under various noise levels by using data augmentation methods while maintaining a robust out-of-distribution performance under various propagation environments and different signal-to-noise ratios (SNR)s. During training, our proposed network only needs 10% of labeled data to converge when compared to supervised learning. Our trained NN can then achieve performance which is only 7% and 2.5% away from mathematical upper bound and fully supervised learning, respectively, with far less computational complexity.