A blind identification algorithm robust to order over estimation
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 05
Subspace methods for the blind identification of multichannel FIRfilters
IEEE Transactions on Signal Processing
Direct estimation of blind zero-forcing equalizers based onsecond-order statistics
IEEE Transactions on Signal Processing
Prediction error method for second-order blind identification
IEEE Transactions on Signal Processing
A blind multichannel identification algorithm robust to orderoverestimation
IEEE Transactions on Signal Processing
Matrix outer-product decomposition method for blind multiplechannel identification
IEEE Transactions on Signal Processing
Space-time block coding for wireless communications: performance results
IEEE Journal on Selected Areas in Communications
Conditional fuzzy clustering for blind channel equalization
Applied Soft Computing
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Blind channel identification and equalization aim at suppressing the need for training sequences and hence respond to the growing demand on bandwidth of modern and future communication systems. Techniques based on second order statistics show good performance but require accurate (but improbable) estimation of the channel order. An algorithm recently proposed by Gazzah et al. corrects this drawback while providing with only those zero-forcing (ZF) equalizers with minimum and maximum delays. However, in the presence of noise, these are not known to have the best performances. Inspired by this approach that processes shifted correlation matrices, we propose a new algorithm that computes ZF equalizers with any desired delay (as well as estimates of the channel response) while, at the same time, can handle over estimated values of the channel order, as justified theoretically and through simulations.