A Variable Step-Size Diffusion LMS Algorithm for Distributed Estimation

We propose a new variable step-size diffusion least mean square algorithm for distributed estimation that adaptively adjusts the step-size in every iteration. For a network application, the proposed method determines a suboptimal step-size at each node to minimize the mean square deviation for the i...

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Bibliographic Details
Published inIEEE transactions on signal processing Vol. 63; no. 7; pp. 1808 - 1820
Main Authors Lee, Han-Sol, Kim, Seong-Eun, Lee, Jae-Woo, Song, Woo-Jin
Format Journal Article
LanguageEnglish
Published IEEE 01.04.2015
Subjects
adaptive networks
Convergence
diffusion LMS algorithm
Distributed estimation
Estimation
Least squares approximations
Noise
Signal processing algorithms
Steady-state
variable step-size
Vectors
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ISSN1053-587X
1941-0476
DOI10.1109/TSP.2015.2401533

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Summary:We propose a new variable step-size diffusion least mean square algorithm for distributed estimation that adaptively adjusts the step-size in every iteration. For a network application, the proposed method determines a suboptimal step-size at each node to minimize the mean square deviation for the intermediate estimate. The algorithm thus adapts the different node environments and profiles across the networks, and requires relatively less user interaction than existing algorithms. In experiments, the algorithm achieves both fast convergence speed and low misadjustment by remarkable improvement in an adaptation stage. We analyze the mean square performance of the proposed algorithm. Also, the proposed algorithm works well even in non-stationary environments.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2015.2401533