Deep Learning for Robust ARIS-Aided Multiuser MIMO Networks With Channel Uncertainty

This work addresses the problem of joint robust transmission, reflection, and reception strategy design in an active reconfigurable intelligent surface (ARIS)-assisted multiuser multiple-input multiple-output (MIMO) system. Specifically, a signal-to-interference-noise (SINR) maximization problem has been formulated by jointly optimizing the transmit beamforming matrix at the base station (BS), the linear reception filters at the users, and the reflection coefficient matrix at the ARIS. The optimization has been performed under constraints on the BS transmit power, the maximum amplification power of the ARIS, and the maximum amplitude coefficients of the ARIS. To jointly optimize RIS-assisted systems, this paper proposes an efficient deep learning (DL) model. Specifically, a multi-layer perceptron (MLP)-based deep neural network (DNN) has been designed to effectively approximate the optimal solution. Further, to handle channel state information (CSI) uncertainties arising from estimation errors and environmental variations, a novel uncertainty injection scheme has been proposed for training DL models. The output of the solution is perturbed through uncertainty injection. The model learns a robust beamforming matrix, linear reception filters, and reflection configurations that maintain high SINR under worst-case channel conditions. Simulation results demonstrate that, for the optimized phase and ARIS configuration, the proposed DL trained with the UI scheme achieves a 26.63% SINR improvement compared to the model trained without UI (WUI). In addition to the ARIS, the performance of the passive reconfigurable intelligent surface has also been analyzed. Further, the time complexity and robustness of the proposed model have been evaluated.