Estimation of OFDM Time-Varying Fading Channels Based on Two-Cross-Coupled Kalman Filters

Abstract

This paper deals with the estimation of rapidly timevarying Rayleigh fading channels in Orthogonal Frequency Division Multiplexing (OFDM) mobile wireless systems. When the fading channel is approximated by an Autoregressive (AR) process, it can be estimated by means of Kalman filtering. Nevertheless, the AR model order has to be selected. In addition, the AR parameters must be estimated. One standard solution to obtain the AR parameters consists in first fitting the AR process autocorrelation function to the theoretical Jakes one and then solving the resulting Yule-Walker Equations (YWE). However, this approach requires the Doppler frequency which is usually unknown. To avoid the estimation of the Doppler frequency, the joint estimation of both the channel and its AR parameters can be addressed. Instead of using the Expectation-Maximization (EM) algorithm which results in large storage requirements and high computational cost, we propose to consider a structure based on two-cross-coupled Kalman filters. It should be noted that the Kalman filters are all the more interactive as the variance of the innovation of the first filter is used to drive the Kalman gain of the second. Simulation results show the effectiveness of this approach especially in high Doppler rate environments.