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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Cet article propose une nouvelle approche pour le traitement conjoint de la détection du signal et de l'estimation du canal basée sur l'algorithme d'espérance-maximisation (EM) dans les communications mobiles à multiplexage par répartition orthogonale de la fréquence (OFDM). Les schémas conventionnels basés sur l'algorithme EM estiment une réponse impulsionnelle de canal à l'aide d'un filtre de Kalman et emploient le modèle de marche aléatoire ou le modèle autorégressif (AR) de premier ordre pour dériver l'équation de processus du filtre. Étant donné que ces modèles supposent que la variation temporelle de la réponse impulsionnelle est un bruit blanc sans prendre en compte aucune propriété d'autocorrélation, la précision de l'estimation du canal se détériore dans des conditions d'évanouissement rapide, ce qui entraîne une augmentation du taux d'erreur de paquet (PER). Pour améliorer la précision de l'estimation des canaux à évanouissement rapide, le schéma proposé utilise un modèle différentiel qui permet de prendre en compte la variation temporelle corrélée en introduisant les différentiels temporels de premier ordre et d'ordre supérieur de la réponse impulsionnelle du canal. De plus, cet article dérive une forme récursive directe de l'estimation de canal le long des axes fréquence et temps afin de réduire la complexité de calcul. Les simulations informatiques de canaux dans des conditions d'évanouissement rapide par trajets multiples démontrent que la méthode proposée est supérieure en termes de PER aux schémas conventionnels qui utilisent le modèle de marche aléatoire.
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Kazushi MURAOKA, Kazuhiko FUKAWA, Hiroshi SUZUKI, Satoshi SUYAMA, "Joint Signal Detection and Channel Estimation Using Differential Models via EM Algorithm for OFDM Mobile Communications" in IEICE TRANSACTIONS on Communications,
vol. E94-B, no. 2, pp. 533-545, February 2011, doi: 10.1587/transcom.E94.B.533.
Abstract: This paper proposes a new approach for the joint processing of signal detection and channel estimation based on the expectation-maximization (EM) algorithm in orthogonal frequency division multiplexing (OFDM) mobile communications. Conventional schemes based on the EM algorithm estimate a channel impulse response using Kalman filter, and employ the random walk model or the first-order autoregressive (AR) model to derive the process equation for the filter. Since these models assume that the time-variation of the impulse response is white noise without considering any autocorrelation property, the accuracy of the channel estimation deteriorates under fast-fading conditions, resulting in an increased packet error rate (PER). To improve the accuracy of the estimation of fast-fading channels, the proposed scheme employs a differential model that allows the correlated time-variation to be considered by introducing the first- and higher-order time differentials of the channel impulse response. In addition, this paper derives a forward recursive form of the channel estimation along both the frequency and time axes in order to reduce the computational complexity. Computer simulations of channels under fast multipath fading conditions demonstrate that the proposed method is superior in PER to the conventional schemes that employ the random walk model.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E94.B.533/_p
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@ARTICLE{e94-b_2_533,
author={Kazushi MURAOKA, Kazuhiko FUKAWA, Hiroshi SUZUKI, Satoshi SUYAMA, },
journal={IEICE TRANSACTIONS on Communications},
title={Joint Signal Detection and Channel Estimation Using Differential Models via EM Algorithm for OFDM Mobile Communications},
year={2011},
volume={E94-B},
number={2},
pages={533-545},
abstract={This paper proposes a new approach for the joint processing of signal detection and channel estimation based on the expectation-maximization (EM) algorithm in orthogonal frequency division multiplexing (OFDM) mobile communications. Conventional schemes based on the EM algorithm estimate a channel impulse response using Kalman filter, and employ the random walk model or the first-order autoregressive (AR) model to derive the process equation for the filter. Since these models assume that the time-variation of the impulse response is white noise without considering any autocorrelation property, the accuracy of the channel estimation deteriorates under fast-fading conditions, resulting in an increased packet error rate (PER). To improve the accuracy of the estimation of fast-fading channels, the proposed scheme employs a differential model that allows the correlated time-variation to be considered by introducing the first- and higher-order time differentials of the channel impulse response. In addition, this paper derives a forward recursive form of the channel estimation along both the frequency and time axes in order to reduce the computational complexity. Computer simulations of channels under fast multipath fading conditions demonstrate that the proposed method is superior in PER to the conventional schemes that employ the random walk model.},
keywords={},
doi={10.1587/transcom.E94.B.533},
ISSN={1745-1345},
month={February},}
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TY - JOUR
TI - Joint Signal Detection and Channel Estimation Using Differential Models via EM Algorithm for OFDM Mobile Communications
T2 - IEICE TRANSACTIONS on Communications
SP - 533
EP - 545
AU - Kazushi MURAOKA
AU - Kazuhiko FUKAWA
AU - Hiroshi SUZUKI
AU - Satoshi SUYAMA
PY - 2011
DO - 10.1587/transcom.E94.B.533
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E94-B
IS - 2
JA - IEICE TRANSACTIONS on Communications
Y1 - February 2011
AB - This paper proposes a new approach for the joint processing of signal detection and channel estimation based on the expectation-maximization (EM) algorithm in orthogonal frequency division multiplexing (OFDM) mobile communications. Conventional schemes based on the EM algorithm estimate a channel impulse response using Kalman filter, and employ the random walk model or the first-order autoregressive (AR) model to derive the process equation for the filter. Since these models assume that the time-variation of the impulse response is white noise without considering any autocorrelation property, the accuracy of the channel estimation deteriorates under fast-fading conditions, resulting in an increased packet error rate (PER). To improve the accuracy of the estimation of fast-fading channels, the proposed scheme employs a differential model that allows the correlated time-variation to be considered by introducing the first- and higher-order time differentials of the channel impulse response. In addition, this paper derives a forward recursive form of the channel estimation along both the frequency and time axes in order to reduce the computational complexity. Computer simulations of channels under fast multipath fading conditions demonstrate that the proposed method is superior in PER to the conventional schemes that employ the random walk model.
ER -