Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
A channel order independent method for blind equalization of MIMO systems
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 05
Blind MMSE equalization: a new direct method
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 05
An analytical constant modulus algorithm
IEEE Transactions on Signal Processing
Projection approximation subspace tracking
IEEE Transactions on Signal Processing
Asymptotic performance analysis of direction-finding algorithmsbased on fourth-order cumulants
IEEE Transactions on Signal Processing
Efficient, high performance, subspace tracking for time-domain data
IEEE Transactions on Signal Processing
Subspace methods for the blind identification of multichannel FIRfilters
IEEE Transactions on Signal Processing
Direct blind MMSE channel equalization based on second-orderstatistics
IEEE Transactions on Signal Processing
Direct estimation of blind zero-forcing equalizers based onsecond-order statistics
IEEE Transactions on Signal Processing
Blind channel approximation: effective channel order determination
IEEE Transactions on Signal Processing
A blind multichannel identification algorithm robust to orderoverestimation
IEEE Transactions on Signal Processing
A subspace algorithm for certain blind identification problems
IEEE Transactions on Information Theory
On MMSE methods for blind identification of OFDM-based SIMO systems
WOCN'09 Proceedings of the Sixth international conference on Wireless and Optical Communications Networks
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We propose a new blind minimum mean square error (MMSE) equalization algorithm of noisy multichannel finite impulse response (FIR) systems, that relies only on second-order statistics. The proposed algorithm offers two important advantages: a low computational complexity and a relative robustness against channel order overestimation errors. Exploiting the fact that the columns of the equalizer matrix filter belong both to the signal subspace and to the kernel of truncated data covariance matrix, the proposed algorithm achieves blindly a direct estimation of the zero-delay MMSE equalizer parameters. We develop a two-step procedure to further improve the performance gain and control the equalization delay. An efficient fast adaptive implementation of our equalizer, based on the projection approximation and the shift invariance property of temporal data covariance matrix, is proposed for reducing the computational complexity from O(n3) to O(qnd), where q is the number of emitted signals, n the data vector length, and d the dimension of the signal subspace. We then derive a statistical performance analysis to compare the equalization performance with that of the optimal MMSE equalizer. Finally, simulation results are provided to illustrate the effectiveness of the proposed blind equalization algorithm.