Proportionate-type normalized least mean square algorithm with gain allocation motivated by minimization of mean-square-weight deviation for colored input

In previous work, a water-filling algorithm was proposed which sought to minimize the mean square error (MSE) at any given time by optimally choosing the gains (i.e. step-sizes) each time instance. This work relied on the assumption that the input signal was white. In this paper, an algorithm is der...

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Bibliographic Details
Published in2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 4124 - 4127
Main Authors Wagner, Kevin T., Doroslovacki, Milos I.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2011
Subjects
Adaptive filtering
Algorithm design and analysis
Convergence
Gain
Minimization
Noise
proportionate-type normalized least mean square (PtNLMS) algorithm
Signal processing algorithms
Steady-state
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Summary:In previous work, a water-filling algorithm was proposed which sought to minimize the mean square error (MSE) at any given time by optimally choosing the gains (i.e. step-sizes) each time instance. This work relied on the assumption that the input signal was white. In this paper, an algorithm is derived which operates when the in put signal is colored. The proposed algorithm minimizes the mean square weight deviation which is important in many applications such as system identification. Additionally, it is shown that by minimizing the mean square weight deviation, an upper bound on the MSE is also minimized. The proposed algorithm offers improved misalignment and learning curve convergence rates relative to other standard algorithms.
ISBN:9781457705380
1457705389
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2011.5947260