TY - GEN
T1 - Steady-state Kalman filtering for channel estimation in OFDM systems utilizing SNR
AU - Liyanage, Maduranga
AU - Sasase, Iwao
PY - 2009/11/23
Y1 - 2009/11/23
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=70449627769&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70449627769&partnerID=8YFLogxK
U2 - 10.1109/ICC.2009.5199491
DO - 10.1109/ICC.2009.5199491
M3 - Conference contribution
AN - SCOPUS:70449627769
SN - 9781424434350
T3 - IEEE International Conference on Communications
BT - Proceedings - 2009 IEEE International Conference on Communications, ICC 2009
T2 - 2009 IEEE International Conference on Communications, ICC 2009
Y2 - 14 June 2009 through 18 June 2009
ER -