Residual Compensation-Based Extreme Learning Machine for MIMO-NOMA Receiver

Abstract

Extreme learning machine (ELM) uses a simple machine learning (ML) architecture that allows its implementation for smart IoT network operations. However, some inaccuracy lies between the predicted and the actual data during the ELM training, which can be caused due to the limitation of the modeling representation. This paper thus investigates a residual compensation-based ELM (RC-ELM) for its application in designing a receiver for MIMO-NOMA aided IoT systems. In RC-ELM, the base ELM layers determine the relationship between the transmitted and received data and the additional layers of the RC-ELM attempt to compensate the error introduced during the training mechanism. The analysis of the appropriate number of compensation layers for training error minimization is conducted on the basis of the bit error rate (BER) and the error vector magnitude (EVM) performances of the RC-ELM training. A minimum BER improvement of 5% for user 1 and 18% for user 2 is shown with with the aid of RC-ELM for a two-IoT user instance. The EVM is marginally increased by 0.0008% for user 1 and 1.61% for user 2 in training stage. Besides, the RC-ELM receiver is also compared to the minimum mean square error (MMSE), the classic ELM and the trained multilayer perceptron (MLP) receivers in terms of BER and EVM. Both of the ELM receivers show improved performances with respect to the other receivers.INDEX TERMS IoT, MIMO-NOMA, machine learning, extreme learning machine, RC-ELM, multilayer perceptron.

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

“…This interval is chosen such that the weight and the bias values fall within the activation function's region of convergence [22]. Based on the study conducted in [27], the number of hidden layer neurons in the traditional ELM is considered to be L = max(N R , N T ). In case of I-ELM receiver, the initial hidden layer size L i and the maximum number of hidden layer neurons, i.e.…”