Blind separation of convolutive mixtures by decorrelation
Signal Processing
A blind source separation framework for detecting CPM sources mixed by a convolutive MIMO filter
Signal Processing - Special section: Advances in signal processing-assisted cross-layer designs
Multiuser channel estimation from higher-order statistical matrix pencil
EURASIP Journal on Applied Signal Processing
Blind source separation based on constant modulus criterion and signal mutual information
Computers and Electrical Engineering
Blind paraunitary equalization
Signal Processing
Blind paraunitary equalization
Signal Processing
An iterative equalization method for nonirreducible MIMO FIR channels
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Blind adaptive equalization of MIMO systems: new recursive algorithms and convergence analysis
IEEE Transactions on Circuits and Systems Part I: Regular Papers
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We discuss the blind deconvolution of multiple input/multiple output (MIMO) linear convolutional mixtures and propose a set of hierarchical criteria motivated by the maximum entropy principle. The proposed criteria are based on the constant-modulus (CM) criterion in order to guarantee that all minima achieve perfectly restoration of different sources. The approach is moreover robust to errors in channel order estimation. Practical implementation is addressed by a stochastic adaptive algorithm with a low computational cost. Complete convergence proofs, based on the characterization of all extrema, are provided. The efficiency of the proposed method is illustrated by numerical simulations