Abstract
Adaptive algorithms for blindly identifying single-input multiple-output (SIMO) systems are appealing because of their computational efficiency and capability of continuously tracking a time-varying system. Adaptive multichannel least-mean-square (MCLMS) algorithms (with and without the unit-norm constraint) are analyzed, and the optimal step size is derived. A simple yet effective variable step-size MCLMS algorithm is proposed, and its performance is evaluated with simulations.
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