Steady-State Kalman Filtering for Channel Estimation in OFDM Systems Utilizing SNR

摘要

Kalman filters are effective channel estimators but they have the drawback of having heavy calculations when filtering needs to be done in each sample. In our paper we obtain the steady-state Kalman gain to estimate the channel state thus eliminating a larger portion of the calculation burden. Steady-state value is calculated by transforming the vector Kalman filtering in to scalar domain by exploiting the filter charactertics when pilot subcarriers are used for channel estimation. Kalman filters operate optimally in the steady-state condition. Therefore by avoiding the convergence period of the Kalman 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. Thus 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 Kalman filter.