Direct blind MMSE channel equalization based on second-order statistics

A family of new MMSE blind channel equalization algorithms based on second-order statistics are proposed. Instead of estimating the channel impulse response, we directly estimate the cross-correlation function needed in Wiener-Hopf filters. We develop several different schemes to estimate the cross-...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2000-04, Vol.48 (4), p.1015-1022
Main Authors: Shen, J., Ding, Z.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Blind equalizers
Blinds
Channel estimation
Channels
Equalization
Estimates
Estimation error
Exact sciences and technology
Impulse response
Information, signal and communications theory
Intersymbol interference
MIMO
Multipath channels
Radiofrequency interference
Separation
Signal and communications theory
Signal processing algorithms
Signal, noise
Statistics
Telecommunications and information theory
Wiener filter
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Summary:A family of new MMSE blind channel equalization algorithms based on second-order statistics are proposed. Instead of estimating the channel impulse response, we directly estimate the cross-correlation function needed in Wiener-Hopf filters. We develop several different schemes to estimate the cross-correlation vector, with which different Wiener filters are derived according to minimum mean square error (MMSE). Unlike many known sub-space methods, these equalization algorithms do not rely on signal and noise subspace separation and are consequently more robust to channel order estimation errors. Their implementation requires no adjustment for either single- or multiple-user systems. They can effectively equalize single-input multiple-output (SIMO) systems and can reduce the multiple-input multiple-output (MIMO) systems into a memoryless signal mixing system for source separation. The implementations of these algorithms on SIMO system are given, and simulation examples are provided to demonstrate their superior performance over some existing algorithms.
ISSN:1053-587X
1941-0476
DOI:10.1109/78.827535