Convergence Analysis of Processing Cost Reduction Method of NLMS Algorithm with Correlated Gaussian Data


IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E79-A   No.7   pp.1044-1050
Publication Date: 1996/07/25
Online ISSN: 
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Digital Signal Processing
NLMS,  convergence characteristics,  processing cost reduction method,  correlated Gaussian data,  

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Reduction of the complexity of the NLMS algorithm has recceived attention in the area of adaptive filtering. A processing cost reduction method, in which the component of the weight vector is updated when the absolute value of the sample is greater than or equal to an arbitrary threshold level, has been proposed. The convergence analysis of the processing cost reduction method with white Gaussian data has been derived. However, a convergence analysis of this method with correlated Gaussian data, which is important for an actual application, is not studied. In this paper, we derive the convergence cheracteristics of the processing cost reduction method with correlated Gaussian data. From the analytical results, it is shown that the range of the gain constant to insure convergence is independent of the correlation of input samples. Also, it is shown that the misadjustment is independent of the correlation of input samples. Moreover, it is shown that the convergence rate is a function of the threshold level and the eigenvalues of the covariance matrix of input samples as well as the gain constant.