Second order modeling for Rayleigh flat fading channel estimation with Kalman Filter
This paper deals with channel estimation over flat fading Rayleigh channel with Jakes' Doppler spectrum. Many estimation algorithms exploit the time domain correlation of the channel by employing Kalman Filter based on an approximation of the time varying channel. A common method used in the literature is based on a first order auto regressive (AR1) model for the channel approximation, combined with a Correlation Matching (CM) criterion to fix the value of the AR1 parameter. In this paper, we propose first to replace the AR1 model by a specific second order (Or2) model (as the one used for the phase estimation in presence of frequency offset) which is more appropriate for slow fading variations. Secondly, we propose a criterion based on the Minimization of the Asymptotic Variance (MAV) of the Kalman estimator to fix the parameter of the Or2 model. Closed form expressions of the optimum Or2 parameter and of the corresponding Mean Square Error (MSE) are derived for a given channel state (Doppler spread, SNR) MSE theoretical analysis and simulation results prove a significant improvement with the Or2 MAV approach compared to more conventional AR1 CM approach (or even AR1 MAV optimized approach) in terms of MSE, especially for slow fading variations.
2011 17th International Conference on Digital Signal Processing (DSP)