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
칼만 필터는 효과적인 채널 추정기이지만 많은 수의 부반송파에 대해 각 샘플에서 필터링을 수행해야 할 때 계산량이 많다는 단점이 있습니다. 본 논문에서는 OFDM에서 파일럿 부반송파의 특성을 활용하여 채널 상태를 추정하기 위한 정상상태 칼만 이득을 구함으로써 계산 부담을 더 많이 줄일 수 있다. 채널 추정을 위해 파일럿 부반송파를 사용할 때 필터 특성을 활용하여 필터링하는 벡터 칼만(Kalman)을 스칼라 영역으로 변환하여 정상 상태 값을 계산합니다. 칼만 필터는 정상 상태에서 최적으로 작동합니다. 따라서 칼만 이득의 수렴 기간을 피함으로써 제안하는 기법은 기존 방법보다 더 나은 성능을 발휘할 수 있다. 또한, 채널의 구동잡음 변화량은 실제적인 상황을 얻기 어려우며, 칼만필터의 올바른 동작을 위해서는 정확한 지식이 중요하다. 따라서 우리는 수신된 신호 대 잡음비(SNR)를 활용하여 구동 잡음 분산에 대한 지식이 없는 상태에서 작동하도록 방식을 확장합니다. 시뮬레이션 결과는 기존 칼만 필터에 비해 상당한 추정기 성능 향상을 얻을 수 있음을 보여줍니다.
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부
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
부
@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},}
부
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 -