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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 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.

- Publication
- IEICE TRANSACTIONS on Communications Vol.E92-B No.7 pp.2452-2460

- Publication Date
- 2009/07/01

- Publicized

- Online ISSN
- 1745-1345

- DOI
- 10.1587/transcom.E92.B.2452

- Type of Manuscript
- PAPER

- Category
- Wireless Communication Technologies

The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.

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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},}

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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 -