Least squares support vector regression-based channel estimation for OFDM systems in the presence of impulsive noise

Abstract

This work aimed to investigate a multipath channel estimation technique for orthogonal frequency division multiplexing (OFDM) systems based on least squares support vector regression (LSSVR) in the presence of Gaussian and impulse noise. Since impulsive noise can considerably affect the performance of communication systems and also due to the time-varying and frequency selectivity of wireless channels, it is unavoidable to have a proper channel estimation and interpolation technique. Therefore, in this contribution, an autoregressive modeled multipath channel was estimated using LSSVR in the presence of impulsive and Gaussian noises. Considering that the proposed method does not need to solve a quadratic programming (QP) problem, it is less complex than standard support vector regression (SVR). The simulation results indicate that the proposed method outperforms standard SVR and the cubic-spline interpolated LS in terms of bit error rate (BER) and mean square error (MSE) criteria.