2D regression channel estimation for equalizing OFDM signals

Ming-Xian Chang, Yu T. Su

Research output: Contribution to journalConference article

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 languageEnglish
Pages (from-to)240-244
Number of pages5
JournalIEEE Vehicular Technology Conference
Volume1
Publication statusPublished - 2000 Jan 1
EventVTC2000: 51st Vehicular Technology Conference 'Shaping History Through Mobile Technologies' - Tokyo, Jpn
Duration: 2000 May 152000 May 18

Fingerprint

Regression Estimation
Channel Estimation
Channel estimation
Orthogonal Frequency Division multiplexing (OFDM)
Orthogonal frequency division multiplexing
Estimate
Bit error rate
Statistics
Nonlinear Regression
Correlation Matrix
Gaussian White Noise
Error Probability
Fading
Mean Squared Error
Frequency Domain
Error Rate
Time Domain
Lower bound
Predict
Error probability

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

Cite this

@article{43b2e962b7294dd689edb4b09442d2dc,
title = "2D regression channel estimation for equalizing OFDM signals",
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).",
author = "Ming-Xian Chang and Su, {Yu T.}",
year = "2000",
month = "1",
day = "1",
language = "English",
volume = "1",
pages = "240--244",
journal = "IEEE Vehicular Technology Conference",
issn = "0740-0551",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

2D regression channel estimation for equalizing OFDM signals. / Chang, Ming-Xian; Su, Yu T.

In: IEEE Vehicular Technology Conference, Vol. 1, 01.01.2000, p. 240-244.

Research output: Contribution to journalConference article

TY - JOUR

T1 - 2D regression channel estimation for equalizing OFDM signals

AU - Chang, Ming-Xian

AU - Su, Yu T.

PY - 2000/1/1

Y1 - 2000/1/1

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).

UR - http://www.scopus.com/inward/record.url?scp=0033719672&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0033719672&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:0033719672

VL - 1

SP - 240

EP - 244

JO - IEEE Vehicular Technology Conference

JF - IEEE Vehicular Technology Conference

SN - 0740-0551

ER -