RIS-Assisted Multi-User MIMO Systems Exploiting Extreme Learning Machine

Massive multiple-input multiple-output (mMIMO), assisted by reconfigurable intelligent surface (RIS), can ensure reliable and energy-efficient data transmission. However, the receiver design for large-scale networks based on traditional mathematical approaches requires complex statistics. Therefore, in this paper, machine learning (ML) approaches are investigated to design receivers for the RIS-assisted multi-user MIMO (muMIMO) systems to avoid complicated channel information requirements. Extreme learning machine (ELM) is an effective ML tool for MIMO receiver design because it simplifies the learning process. However, the learning performance of the ELM can get affected by the random choice of its hidden layer size. To address this issues, this paper proposes an incremental ELM (I-ELM) based receiver for the RIS-mu-MIMO system. The proposed receiver computes the weights between the hidden and the output layer based on the automated incremental addition of hidden neurons and provided conditions. The suggested receiver is contrasted with the multilayer perceptron (MLP), conventional ELM, and minimum mean square error (MMSE) receivers. The simulation results show that the throughput performance of the proposed receiver is satisfactory.