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The Expectation and Sparse Maximization Algorithm  

Barembruch, Steffen (Institut des Telecommunications, Telecom ParisTech)
Scaglione, Anna (Department ECE at University of California Davis)
Moulines, Eric (Institut des Telecommunications, Telecom ParisTech)
Publication Information
Journal of Communications and Networks / v.12, no.4, 2010 , pp. 317-329 More about this Journal
Abstract
In recent years, many sparse estimation methods, also known as compressed sensing, have been developed. However, most of these methods presume that the measurement matrix is completely known. We develop a new blind maximum likelihood method-the expectation-sparse-maximization (ESpaM) algorithm-for models where the measurement matrix is the product of one unknown and one known matrix. This method is a variant of the expectation-maximization algorithm to deal with the resulting problem that the maximization step is no longer unique. The ESpaM algorithm is justified theoretically. We present as well numerical results for two concrete examples of blind channel identification in digital communications, a doubly-selective channel model and linear time invariant sparse channel model.
Keywords
Compressive sensing (CS); deconvolution; multipath channels; smoothing methods;
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