A globally convergent approach for blind MIMO adaptive deconvolution

• Published in: IEEE transactions on signal processing Vol. 49; no. 6; pp. 1166 - 1178
• Main Authors: Touzni, A., Fijalkow, I., Larimore, M.G., Treichler, J.R.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-06-2001; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithm; Applied sciences; Blinds; Channels; Computational efficiency; Convergence; Convolution; Criteria; Deconvolution; Entropy; Exact sciences and technology; Information, signal and communications theory; Interference suppression; Mathematical models; Maximum entropy; MIMO; Miscellaneous; Robustness; Signal and communications theory; Stochastic processes; Telecommunications and information theory; Performance evaluation; MIMO system; Deconvolution; Adaptive algorithm; Probabilistic approach; Signal processing; Numerical simulation; Method of maximum entropy; Hierarchical system; Adaptive method; Implementation; Convergence
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/78.923299

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.