Proportionate adaptive algorithm for nonsparse systems based on Krylov subspace and constrained optimization

Authors:
Masahiro Yukawa;Wolfgang Utschick
Affiliations:
Amari Research Unit, Brain Science Institute, RIKEN, Japan;Associate Institute for Signal Processing, Munich University of Technology, Germany
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

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Abstract

In this paper, we propose an efficient design of proportionality factors in the recently established algorithm named Krylov-proportionate normalized least mean-square (KPNLMS), which is an extention of the PNLMS algorithm to nonsparse (or dispersive) unknown systems by means of a Krylov subspace. The designing task takes a form of minimizing the number of iterations that is needed for an upper bound of the system mismatch to reach a specified target value. The minimization is performed under several constraints related to numerical stability, computational requirements, and nonnegativity, and its closed-form solution is derived. Numerical examples demonstrate that the proposed design significantly reduces the number of iterations needed to achieve target values of system mismatch especially when a low level of system mismatch is required.