Authors: P. Sudheesh; M. Jayakumar
Addresses: Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore, India ' Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore, India
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.
Keywords: nonlinear channel estimation; MIMO-OFDM system; Kalman filter; KF; extended Kalman filter; EKF; particle filter; PF.
International Journal of Advanced Intelligence Paradigms, 2019 Vol.13 No.3/4, pp.420 - 429
Received: 03 Dec 2016
Accepted: 23 Jul 2017
Published online: 28 Aug 2019 *