The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Les filtres de Kalman sont des estimateurs de canal efficaces mais ils présentent l'inconvénient de nécessiter des calculs lourds lorsqu'un filtrage doit être effectué dans chaque échantillon pour un grand nombre de sous-porteuses. Dans notre article, nous obtenons le gain de Kalman en régime permanent pour estimer l'état du canal en utilisant les caractéristiques des sous-porteuses pilotes dans l'OFDM, ce qui permet d'éliminer une plus grande partie de la charge de calcul. La valeur à l'état stable est calculée en transformant le filtrage vectoriel de Kalman en domaine scalaire en exploitant les caractéristiques du filtre lorsque des sous-porteuses pilotes sont utilisées pour l'estimation du canal. Les filtres de Kalman fonctionnent de manière optimale en régime permanent. Par conséquent, en évitant la période de convergence du gain de Kalman, le schéma proposé est capable de mieux fonctionner que la méthode conventionnelle. De plus, la variance du bruit moteur du canal est difficile à obtenir dans des situations pratiques et une connaissance précise est importante pour le bon fonctionnement du filtre de Kalman. Par conséquent, nous étendons notre système pour qu'il fonctionne en l'absence de connaissance de la variance du bruit de conduite en utilisant le rapport signal sur bruit (SNR) reçu. Les résultats de simulation montrent qu'un gain considérable de performances de l'estimateur peut être obtenu par rapport au filtre de Kalman conventionnel.
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Maduranga LIYANAGE, Iwao SASASE, "Steady-State Kalman Filtering for Channel Estimation in OFDM Systems for Rayleigh Fading Channels" in IEICE TRANSACTIONS on Communications,
vol. E92-B, no. 7, pp. 2452-2460, July 2009, doi: 10.1587/transcom.E92.B.2452.
Abstract: Kalman 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 Kalman 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 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. 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 Kalman filter.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E92.B.2452/_p
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@ARTICLE{e92-b_7_2452,
author={Maduranga LIYANAGE, Iwao SASASE, },
journal={IEICE TRANSACTIONS on Communications},
title={Steady-State Kalman Filtering for Channel Estimation in OFDM Systems for Rayleigh Fading Channels},
year={2009},
volume={E92-B},
number={7},
pages={2452-2460},
abstract={Kalman 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 Kalman 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 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. 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 Kalman filter.},
keywords={},
doi={10.1587/transcom.E92.B.2452},
ISSN={1745-1345},
month={July},}
Copier
TY - JOUR
TI - Steady-State Kalman Filtering for Channel Estimation in OFDM Systems for Rayleigh Fading Channels
T2 - IEICE TRANSACTIONS on Communications
SP - 2452
EP - 2460
AU - Maduranga LIYANAGE
AU - Iwao SASASE
PY - 2009
DO - 10.1587/transcom.E92.B.2452
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E92-B
IS - 7
JA - IEICE TRANSACTIONS on Communications
Y1 - July 2009
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 for a large number of subcarriers. In our paper we obtain the steady-state Kalman 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 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. 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 Kalman filter.
ER -