Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Fundamentals of WiMAX: Understanding Broadband Wireless Networking (Prentice Hall Communications Engineering and Emerging Technologies Series)
A comparison of pilot-aided channel estimation methods for OFDMsystems
IEEE Transactions on Signal Processing
CHANNEL ESTIMATION FOR WIRELESS OFDM SYSTEMS
IEEE Communications Surveys & Tutorials
Pilot tone selection for channel estimation in a mobile OFDM system
IEEE Transactions on Consumer Electronics
IEEE Transactions on Consumer Electronics
Channel estimation for OFDM with fast fading channels by modified Kalman filter
IEEE Transactions on Consumer Electronics
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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 characteristics 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.