Steady-state Kalman filtering for channel estimation in OFDM systems for Rayleigh fading channels

Kaiman filters are effective channel estimators but they have the drawback of having heavy calculations when filtering needs to be done in each sample for a large number of subcarriers. In our paper we obtain the steady-state Kaiman, gain to estimate the channel state by utilizing the characteristics of pilot subcarriers in OFDM, and thus a larger portion, of the calculation burden can be eliminated. Steady-state value is calculated by transforming the vector Kaiman filtering in to scalar domain by exploiting the filter charactertics when pilot subcarriers are used for channel estimation. Kaiman filters operate optimally in the steady-state condition. Therefore by avoiding the convergence period, of the Kaiman gain, the proposed scheme is able to perform, better than the conventional method. Also, driving noise variance of the channel is difficult to obtain, practical situations and accurate knowledge is important for the proper operation of the Kalman filter. Therefore, we extend our scheme to operate in the absence of the knowledge of driving noise variance by utilizing received Signal-to-Noise Ratio (SNR). Simulation results show considerable estimator performance gain can. be obtained compared to the conventional Kaiman filter.