Nonlinear channel tracking of a high mobility wireless communication system. (28th August 2019)

• Record Type: Journal Article
• Authors: Sudheesh, P.; Jayakumar, M.
• Is Part Of: International journal of advanced intelligence paradigms. Volume 13:Number 3/4(2019)
• Page Start: 420
• Page End: 429
• Publication Date: 2019-08-28
• Subjects: nonlinear channel estimation -- MIMO-OFDM system -- Kalman filter -- KF -- extended Kalman filter -- EKF -- particle filter -- PF; Artificial intelligence -- Periodicals; Machine theory -- Periodicals; Fuzzy logic -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-0386

Abstract

Recently evolved wireless communication systems incorporate the use of multiple input multiple output (MIMO) systems to overcome the effects of channel fading. Orthogonal frequency division multiplexing (OFDM) is moreover used to overcome inter-symbol interference (ISI) to ensure effective signal transmission. The channel parameters in wireless communication systems are generally nonlinear. Channel estimation techniques for nonlinear systems include unscented Kalman filter (UKF), Kalman filter (KF) and extended Kalman filter (EKF). The Kalman filter is used for linear channel estimation whereas the EKF and UKF are applicable for nonlinear systems as well. Particle filter is a type of sequential Monte Carlo (SMC) method which uses sequential importance sampling (SIS) technique to effectively track a nonlinear system. Particle filter (PF) is an efficient method of tracking, which is able to deal with non-Gaussian and nonlinear systems. In this paper, we estimate the channel parameters of a fast time varying MIMO-OFDM system using particle filter. The proposed scheme considers a first order auto-regressive (AR) system model. A Rayleigh fading channel for mobile systems which incorporates the Doppler shift that occurs in a mobile environment is used. The performance of the particle filter is compared with the other estimation methods like Kalman filter and extended Kalman filter. The mean square error (MSE) as a function of the signal to noise ratio (SNR) is plotted to compare the performance of the particle filter with other systems.