Joint Signal Detection and Channel Estimation Using Differential Models via EM Algorithm for OFDM Mobile Communications

Kazushi MURAOKA; Kazuhiko FUKAWA; Hiroshi SUZUKI; Satoshi SUYAMA

doi:10.1587/transcom.E94.B.533

Joint Signal Detection and Channel Estimation Using Differential Models via EM Algorithm for OFDM Mobile Communications

Kazushi MURAOKA, Kazuhiko FUKAWA, Hiroshi SUZUKI, Satoshi SUYAMA

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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.

Publication: IEICE TRANSACTIONS on Communications Vol.E94-B No.2 pp.533-545

Publication Date: 2011/02/01

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Online ISSN: 1745-1345

DOI: 10.1587/transcom.E94.B.533

Type of Manuscript: PAPER

Category: Wireless Communication Technologies

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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 -