A Design of Extreme Learning Machine Based Receiver for 2×2 MIMO-OFDM System

Machine learning has raised the research interest for future communication network. The implementation of machine learning technologies such as Deep Neural Network (DNN), Support Vector Machine (SVM) and Extreme Learning Machine (ELM) have enabled the derivation of channel estimation techniques which provide more accurate estimation in comparison with accuracy provided by other well-known estimation techniques. ELM has faster learning ability compared to that of the other aforementioned technologies. This paper focuses on the ELM based receiver design for 2×2 MIMO OFDM system. The determination of the output layer neuron, a component in ELM technique, on the basis of symbol type (number of bits per symbol) has been proposed. The performance of the proposed receiver is evaluated in terms of Bit Error Rate (BER) and compared with that of the Minimum Mean Square Error (MMSE) based receiver. Improved performance has been achieved by means of the proposed scheme.