Sign-Regressor Adaptive Filtering Algorithms Using Averaged Iterates and Observations

Motivated by the resurgent interest in efficient adaptive signal processing algorithms for interference suppression in wireless CDMA (Code Division Multiple Access) communication networks, this paper is concerned with asymptotic properties of adaptive filtering algorithms. Our focus is on improving efficiency of sign-regressor procedures, which are known to have reduced complexity compared with the usual LMS algorithms and better performance compared with the sign-error procedures. In view of the recent developments in iterate averaging for stochastic approximation methods, algorithms that include both iterate and observation averaging are suggested. It is shown that such algorithms converge to the true parameter and the convergence rate is optimal.

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