Symbol-variance Feedback Equalizer for Turbo Equalization

WU Yanbo; FANG Xiaofang; ZHU Min

doi:10.11999/JEIT150825

Volume 38 Issue 3

Mar. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(3): 694-699

WU Yanbo, FANG Xiaofang, ZHU Min. Symbol-variance Feedback Equalizer for Turbo Equalization[J]. Journal of Electronics & Information Technology, 2016, 38(3): 694-699. doi: 10.11999/JEIT150825

Citation:

WU Yanbo, FANG Xiaofang, ZHU Min. Symbol-variance Feedback Equalizer for Turbo Equalization[J]. Journal of Electronics & Information Technology, 2016, 38(3): 694-699. doi: 10.11999/JEIT150825

WU Yanbo, FANG Xiaofang, ZHU Min. Symbol-variance Feedback Equalizer for Turbo Equalization[J]. Journal of Electronics & Information Technology, 2016, 38(3): 694-699. doi: 10.11999/JEIT150825

Citation:

WU Yanbo, FANG Xiaofang, ZHU Min. Symbol-variance Feedback Equalizer for Turbo Equalization[J]. Journal of Electronics & Information Technology, 2016, 38(3): 694-699. doi: 10.11999/JEIT150825

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Symbol-variance Feedback Equalizer for Turbo Equalization

doi: 10.11999/JEIT150825

Funds:

The National Natural Science Foundation of China (61471351), The National 863 Program of China (2009AA 093301)

Received Date: 2015-07-09
Rev Recd Date: 2015-12-08
Publish Date: 2016-03-19

Abstract

Abstract

A novel Symbol-Variance Feedback Equalizer (SVEF) algorithm is proposed to reduce the computational complexity of the equalizer in Turbo equalization. The derivation of the algorithm is based on the Taylor expansion of the Linear Minimum Mean Squared Error (LMMSE) estimation function. In the proposed scheme, the initial estimates are obtained from the time-invariant equalizer, then the estimates are weighted by the a priori symbol variances and finally filtered by a time-invariant filter to obtain better estimates. As the time-variant a priori symbol variances are utilized, the performance of the proposed equalizer is much closer to that of the exact MMSE linear equalizer. Simulation results show that the Signal-to-Noise Ratio (SNR) loss of the proposed scheme in Proakis C channel is reduced to 0.17 dB from 0.83 dB compared to the various time-invariant MMSE Turbo equalization, and its computational complexity can be reduced to logarithmical order by implementation based on the fast Fourier transform.
- Turbo equalization,
- Soft-Input Soft-Output (SISO) equalizer,
- Minimum Mean Squared Error (MMSE) linear equalizer

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References(26)

References

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