2D regression channel estimation for equalizing OFDM signals

Ming-Xian Chang; Yu T. Su

2D regression channel estimation for equalizing OFDM signals

Ming-Xian Chang, Yu T. Su

Department of Electrical Engineering

Research output: Contribution to journal › Conference article › peer-review

12 Citations (Scopus)

Abstract

In this paper, we present a channel estimation method for Orthogonal Frequency Division Multiplexing (OFDM) signals. Our method is based on a two-dimensional nonlinear regression method, taking into account the correlations of the fading process in both time and frequency domains. We derive a general bit error rate (BER) expression which can also used to predict the performance of many other OFDM channel estimates. The performance of this new estimate is very close to the theoretical bit error probability lower bound that is obtained by assuming that the channel response is perfectly known. Unlike linear minimum mean-squared-error (LMMSE) channel estimates, it needs not to know or estimate channel statistics like channel correlation matrix and SNR hence is insensitive to additive white Gaussian noise (AWGN).

Original language	English
Pages (from-to)	240-244
Number of pages	5
Journal	IEEE Vehicular Technology Conference
Volume	1
Publication status	Published - 2000 Jan 1
Event	VTC2000: 51st Vehicular Technology Conference 'Shaping History Through Mobile Technologies' - Tokyo, Jpn Duration: 2000 May 15 → 2000 May 18

All Science Journal Classification (ASJC) codes

Computer Science Applications
Electrical and Electronic Engineering
Applied Mathematics

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abstract = "In this paper, we present a channel estimation method for Orthogonal Frequency Division Multiplexing (OFDM) signals. Our method is based on a two-dimensional nonlinear regression method, taking into account the correlations of the fading process in both time and frequency domains. We derive a general bit error rate (BER) expression which can also used to predict the performance of many other OFDM channel estimates. The performance of this new estimate is very close to the theoretical bit error probability lower bound that is obtained by assuming that the channel response is perfectly known. Unlike linear minimum mean-squared-error (LMMSE) channel estimates, it needs not to know or estimate channel statistics like channel correlation matrix and SNR hence is insensitive to additive white Gaussian noise (AWGN).",

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T1 - 2D regression channel estimation for equalizing OFDM signals

AU - Chang, Ming-Xian

AU - Su, Yu T.

PY - 2000/1/1

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N2 - In this paper, we present a channel estimation method for Orthogonal Frequency Division Multiplexing (OFDM) signals. Our method is based on a two-dimensional nonlinear regression method, taking into account the correlations of the fading process in both time and frequency domains. We derive a general bit error rate (BER) expression which can also used to predict the performance of many other OFDM channel estimates. The performance of this new estimate is very close to the theoretical bit error probability lower bound that is obtained by assuming that the channel response is perfectly known. Unlike linear minimum mean-squared-error (LMMSE) channel estimates, it needs not to know or estimate channel statistics like channel correlation matrix and SNR hence is insensitive to additive white Gaussian noise (AWGN).

AB - In this paper, we present a channel estimation method for Orthogonal Frequency Division Multiplexing (OFDM) signals. Our method is based on a two-dimensional nonlinear regression method, taking into account the correlations of the fading process in both time and frequency domains. We derive a general bit error rate (BER) expression which can also used to predict the performance of many other OFDM channel estimates. The performance of this new estimate is very close to the theoretical bit error probability lower bound that is obtained by assuming that the channel response is perfectly known. Unlike linear minimum mean-squared-error (LMMSE) channel estimates, it needs not to know or estimate channel statistics like channel correlation matrix and SNR hence is insensitive to additive white Gaussian noise (AWGN).

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AN - SCOPUS:0033719672

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EP - 244

JO - IEEE Vehicular Technology Conference

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T2 - VTC2000: 51st Vehicular Technology Conference 'Shaping History Through Mobile Technologies'

Y2 - 15 May 2000 through 18 May 2000

ER -

2D regression channel estimation for equalizing OFDM signals

Abstract

All Science Journal Classification (ASJC) codes

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