An exponentiated gradient adaptive algorithm for blind identification of sparse SIMO systems

Sparse impulse responses are encountered in many acoustic and wireless channels. Recently, a class of exponentiated gradient (EG) algorithms has been proposed. One of the algorithms belonging to this class, the so-called EG± algorithm, converges and tracks much better than the classical stochastic gradient, or LMS, algorithm for sparse impulse responses. We apply this technique to blind identification of a sparse SIMO system and develop the multichannel EG± algorithm. A simple experiment demonstrates its advantage in convergence compared to the MCLMS algorithm.