End-to-End Deep Learning assisted by Reconfigurable Intelligent Surface for uplink MU massive MIMO under PA Non-Linearities

This paper investigates a novel high-performance autoencoder based deep learning approach for Multi-User massive MIMO uplink systems assisted by a Reconfigurable Intelligent Surface (RIS) in which the users are equipped by Power Amplifiers (PA) and aim to communicate with the base station. Indeed, the communication process is formulated in the form of a Deep Neuronal Network (DNN). To handle these scenarios, we have designed a DNN network that includes two components. the first is an encoder intended to process the nonlinear distortions of the PA. The second is a decoder consisting of two fundamental steps. 1) A classic Minimum Mean Square Error linear decoder to decode the information transmitted by the users. 2) The neural network decoder to minimize interference. The results of the numerical simulation illustrate that the proposed method offers a significant improvement in error performance in comparison with the different basic schemes.