Nonlinear Channel Estimation for OFDM System by Complex LS-SVM under High Mobility Conditions

TL;DR

This paper introduces a nonlinear channel estimation method using complex LS-SVM for OFDM systems, improving accuracy and robustness in high mobility LTE scenarios with multipath and impulse noise.

Contribution

It presents a novel LS-SVM-based nonlinear estimator tailored for high mobility OFDM channels, outperforming traditional methods in dynamic conditions.

Findings

Enhanced channel tracking accuracy in high-speed scenarios

Robustness against non-Gaussian impulse noise

Superior performance compared to conventional LS estimation

Abstract

A nonlinear channel estimator using complex Least Square Support Vector Machines (LS-SVM) is proposed for pilot-aided OFDM system and applied to Long Term Evolution (LTE) downlink under high mobility conditions. The estimation algorithm makes use of the reference signals to estimate the total frequency response of the highly selective multipath channel in the presence of non-Gaussian impulse noise interfering with pilot signals. Thus, the algorithm maps trained data into a high dimensional feature space and uses the structural risk minimization (SRM) principle to carry out the regression estimation for the frequency response function of the highly selective channel. The simulations show the effectiveness of the proposed method which has good performance and high precision to track the variations of the fading channels compared to the conventional LS method and it is robust at high speed mobility.