Online Extreme Learning Machine-Based Channel Estimation and Equalization for OFDM Systems

Abstract

Machine learning-based channel estimation and equalization methods may improve the robustness and bit error rate (BER) performance of communication systems. However, the implementation of these methods has been blocked by some limitations, mainly including channel model-based offline training and high-computational complexity for training deep neural network (DNN). To overcome those limitations, we propose an online fully complex extreme learning machine (C-ELM)-based channel estimation and equalization scheme with a single hidden layer feedforward network (SLFN) for orthogonal frequency-division multiplexing (OFDM) systems against fading channels and the nonlinear distortion resulting from an high-power amplifier (HPA). Computer simulations show that the proposed scheme can acquire the information of channels accurately and has the ability to resist nonlinear distortion and fading without pre-training and feedback link between receiver and transmitter. Furthermore, the robustness of the proposed scheme is well investigated by extensive simulations in various fading channels, and its excellent generalization ability is also discussed and compared with the DNN.