Model-based channel estimation for OFDM signals in Rayleigh fading

Ming Xian Chang; Yu T. Su

doi:10.1109/26.996066

Model-based channel estimation for OFDM signals in Rayleigh fading

Ming Xian Chang, Yu T. Su

Institute of Computer and Communication Engineering

Research output: Contribution to journal › Article › peer-review

112 Citations (Scopus)

Abstract

This paper proposes a robust pilot-assisted channel estimation method for orthogonal frequency division multiplexing (OFDM) signals in Rayleigh fading. Our estimation method is based on nonlinear regression channel models. Unlike the linear minimum mean-squared error (LMMSE) channel estimate, the method proposed does not have to know or estimate channel statistics like the channel correlation matrix and the average signal-to-noise ratio (SNR) per bit. Numerical results indicate that the performance of the proposed channel estimator is very close to the theoretical bit error propagation lower bound that is obtained by a receiver with perfect channel response information.

Original language	English
Pages (from-to)	540-544
Number of pages	5
Journal	IEEE Transactions on Communications
Volume	50
Issue number	4
DOIs	https://doi.org/10.1109/26.996066
Publication status	Published - 2002 Apr

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering

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title = "Model-based channel estimation for OFDM signals in Rayleigh fading",

abstract = "This paper proposes a robust pilot-assisted channel estimation method for orthogonal frequency division multiplexing (OFDM) signals in Rayleigh fading. Our estimation method is based on nonlinear regression channel models. Unlike the linear minimum mean-squared error (LMMSE) channel estimate, the method proposed does not have to know or estimate channel statistics like the channel correlation matrix and the average signal-to-noise ratio (SNR) per bit. Numerical results indicate that the performance of the proposed channel estimator is very close to the theoretical bit error propagation lower bound that is obtained by a receiver with perfect channel response information.",

author = "Chang, {Ming Xian} and Su, {Yu T.}",

note = "Funding Information: Paper approved by A. Goldsmith, the Editor for Wireless Communication of the IEEE Communications Society. Manuscript received August 15, 2000; revised April 15, 2001, and August 15, 2001. This work was supported in part by the MOE{\textquoteright}s Program of Excellence under Contract 89-E-FA06-2-4 and in part by the National Science Council of Taiwan under Grant NSC86-2221-E-009-058. This paper was presented in part at the IEEE VTC2000-Spring, Tokyo, Japan, May 2000.",

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AU - Chang, Ming Xian

AU - Su, Yu T.

N1 - Funding Information: Paper approved by A. Goldsmith, the Editor for Wireless Communication of the IEEE Communications Society. Manuscript received August 15, 2000; revised April 15, 2001, and August 15, 2001. This work was supported in part by the MOE’s Program of Excellence under Contract 89-E-FA06-2-4 and in part by the National Science Council of Taiwan under Grant NSC86-2221-E-009-058. This paper was presented in part at the IEEE VTC2000-Spring, Tokyo, Japan, May 2000.

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N2 - This paper proposes a robust pilot-assisted channel estimation method for orthogonal frequency division multiplexing (OFDM) signals in Rayleigh fading. Our estimation method is based on nonlinear regression channel models. Unlike the linear minimum mean-squared error (LMMSE) channel estimate, the method proposed does not have to know or estimate channel statistics like the channel correlation matrix and the average signal-to-noise ratio (SNR) per bit. Numerical results indicate that the performance of the proposed channel estimator is very close to the theoretical bit error propagation lower bound that is obtained by a receiver with perfect channel response information.

AB - This paper proposes a robust pilot-assisted channel estimation method for orthogonal frequency division multiplexing (OFDM) signals in Rayleigh fading. Our estimation method is based on nonlinear regression channel models. Unlike the linear minimum mean-squared error (LMMSE) channel estimate, the method proposed does not have to know or estimate channel statistics like the channel correlation matrix and the average signal-to-noise ratio (SNR) per bit. Numerical results indicate that the performance of the proposed channel estimator is very close to the theoretical bit error propagation lower bound that is obtained by a receiver with perfect channel response information.

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