Deterministic blind subspace MIMO equalization
EURASIP Journal on Applied Signal Processing
Blind adaptive channel equalization with performance analysis
EURASIP Journal on Applied Signal Processing
EURASIP Journal on Advances in Signal Processing
Calculating Inverse Filters for Speech Dereverberation
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Blind estimation of signal in periodic long-code DSSS communications
SARNOFF'09 Proceedings of the 32nd international conference on Sarnoff symposium
Speech dereverberation based on variance-normalized delayed linear prediction
IEEE Transactions on Audio, Speech, and Language Processing - Special issue on processing reverberant speech: methodologies and applications
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Equalization for digital communications constitutes a very particular blind deconvolution problem in that the received signal is cyclostationary. Oversampling (OS) (w.r.t. the symbol rate) of the cyclostationary received signal leads to a stationary vector-valued signal (polyphase representation (PR)). OS also leads to a fractionally-spaced channel model and equalizer. In the PR, channel and equalizer can be considered as an analysis and synthesis filter bank. Zero-forcing (ZF) equalization corresponds to a perfect-reconstruction filter bank. We show that in the OS case FIR ZF equalizers exist for a FIR channel. In the PR, the multichannel linear prediction of the noiseless received signal becomes singular eventually, reminiscent of the single-channel prediction of a sum of sinusoids. As a result, the channel can be identified from the received signal second-order statistics by linear prediction in the noise-free case, and by using the Pisarenko method when there is additive noise. In the given data case, MUSIC (subspace) or ML techniques can be applied.