Diffusion Signed LMS Algorithms and Their Performance Analyses for Cyclostationary White Gaussian Inputs

As one of the signed variants of the diffusion least mean square (DLMS) algorithm over networks, the diffusion sign error algorithm has been presented in previous reference. In this paper, we propose two novel signed variants of the DLMS algorithm, i.e., the diffusion signed regressor algorithm and...

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Vydáno v:IEEE access Ročník 5; s. 18876 - 18894
Hlavní autoři: Wang, Wenyuan, Zhao, Haiquan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.01.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptation models
adaptive filter
Algorithm design and analysis
Algorithms
Analytical models
Computational modeling
cyclostationary signals
Diffusion
Distributed algorithms
Distributed network
Nonstationary environments
Parameter identification
Performance analysis
Random walk
Regression analysis
sign algorithm
Signal processing algorithms
stochastic model
ISSN:2169-3536, 2169-3536
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Shrnutí:As one of the signed variants of the diffusion least mean square (DLMS) algorithm over networks, the diffusion sign error algorithm has been presented in previous reference. In this paper, we propose two novel signed variants of the DLMS algorithm, i.e., the diffusion signed regressor algorithm and the diffusion sign-sign algorithm. Moreover, this paper analyzes the performance of these three signed variants of the DLMS algorithm for cyclostationary white Gaussian inputs which have periodically time-varying variances. It is assumed that the distributed algorithms are in non-stationary environments. Specifically, the unknown parameter to be identified is time-varying according to the standard random walk model. The analysis models in terms of mean weight behavior and mean square performance are provided, in which, we can find some interesting results. Finally, simulations are carried out to verify the correctness of the proposed analysis model.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2017.2733766