Low-complexity channel estimator based on windowed DFT and scalar Wiener filter for OFDM systems

B. Yang, Z. Cao, Khaled Letaief

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

The paper presents a low complexity channel estimator based on windowed discrete Fourier transform (DFT) and scalar Wiener filter for Orthogonal Frequency Division Multiplexing (OFDM) mobile communications systems. In the method, a generalized Hanning window is applied to the channel frequency response observation vector in the frequency domain to reduce the spectral leakage, and a scalar Wiener filter is applied to the effective channel impulse response in the time domain to suppress the channel noise. Analysis results show that the proposed method performance is close to the optimal Minimum Mean Square Error (MMSE) estimator and is much better than the direct DFT based estimator. Compared with the optimal MMSE estimator, however, the computation load of the proposed method can be significantly reduced because the IDFT/DFT transforms can be implemented with the fast algorithms IFFT/FFT.

Original languageEnglish
Pages (from-to)1643-1647
Number of pages5
JournalIEEE International Conference on Communications
Volume6
Publication statusPublished - 2001
Externally publishedYes

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Discrete Fourier transforms
Orthogonal frequency division multiplexing
Mean square error
Mobile telecommunication systems
Impulse response
Fast Fourier transforms
Frequency response

ASJC Scopus subject areas

  • Media Technology

Cite this

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abstract = "The paper presents a low complexity channel estimator based on windowed discrete Fourier transform (DFT) and scalar Wiener filter for Orthogonal Frequency Division Multiplexing (OFDM) mobile communications systems. In the method, a generalized Hanning window is applied to the channel frequency response observation vector in the frequency domain to reduce the spectral leakage, and a scalar Wiener filter is applied to the effective channel impulse response in the time domain to suppress the channel noise. Analysis results show that the proposed method performance is close to the optimal Minimum Mean Square Error (MMSE) estimator and is much better than the direct DFT based estimator. Compared with the optimal MMSE estimator, however, the computation load of the proposed method can be significantly reduced because the IDFT/DFT transforms can be implemented with the fast algorithms IFFT/FFT.",
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AU - Yang, B.

AU - Cao, Z.

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N2 - The paper presents a low complexity channel estimator based on windowed discrete Fourier transform (DFT) and scalar Wiener filter for Orthogonal Frequency Division Multiplexing (OFDM) mobile communications systems. In the method, a generalized Hanning window is applied to the channel frequency response observation vector in the frequency domain to reduce the spectral leakage, and a scalar Wiener filter is applied to the effective channel impulse response in the time domain to suppress the channel noise. Analysis results show that the proposed method performance is close to the optimal Minimum Mean Square Error (MMSE) estimator and is much better than the direct DFT based estimator. Compared with the optimal MMSE estimator, however, the computation load of the proposed method can be significantly reduced because the IDFT/DFT transforms can be implemented with the fast algorithms IFFT/FFT.

AB - The paper presents a low complexity channel estimator based on windowed discrete Fourier transform (DFT) and scalar Wiener filter for Orthogonal Frequency Division Multiplexing (OFDM) mobile communications systems. In the method, a generalized Hanning window is applied to the channel frequency response observation vector in the frequency domain to reduce the spectral leakage, and a scalar Wiener filter is applied to the effective channel impulse response in the time domain to suppress the channel noise. Analysis results show that the proposed method performance is close to the optimal Minimum Mean Square Error (MMSE) estimator and is much better than the direct DFT based estimator. Compared with the optimal MMSE estimator, however, the computation load of the proposed method can be significantly reduced because the IDFT/DFT transforms can be implemented with the fast algorithms IFFT/FFT.

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