TY - JOUR
T1 - Diffusion Bayesian Subband Adaptive Filters for Distributed Estimation over Sensor Networks
AU - Huang, Fuyi
AU - Zhang, Jiashu
AU - Zhang, Sheng
AU - Chen, Hongyang
AU - Poor, H. Vincent
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grants 61671392, 62071396, and 61801401, in part by the Fundamental Research Funds for the Central Universities under Grant XJ2021KJZK003, in part by the funding project of Zhejiang Lab under Grant 2020LC0PI01, and in part by the U.S. National Science Foundation under Grants CCF-0939370 and CCF-1908308.
Publisher Copyright:
© 1972-2012 IEEE.
PY - 2021/10/1
Y1 - 2021/10/1
N2 - Sensor networks are an indispensable part of the Internet of Things (IoT), where sensors perform data acquisition and information processing tasks to obtain the parameters of interest so that IoT-based monitoring, diagnosis and other systems respond quickly to the changing conditions, instantaneous faults, etc. Distributed estimation algorithms are usually employed to estimate the parameters of interest in these IoT-based applications. However, when sensor networks have highly correlated input signals and nonstationary behavior in which the parameters of interest are time-varying, conventional distributed estimation algorithms suffer from severely degraded learning performance due to the large eigenvalue spread in the covariance matrix of the input signals and the random perturbation of the parameters of interest. To address these problems, this paper proposes two diffusion Bayesian subband adaptive filter (DBSAF) algorithms from a Bayesian learning perspective. As the highly-correlated input signal is whitened in a multiband structure and an estimate of the uncertainty in the parameters of interest is obtained by performing Bayesian inference, the proposed DBSAF algorithms are able to achieve better learning performance in comparison with the competing diffusion algorithms. The transient and steady-state mean square error performance of the proposed DBSAF algorithms are analyzed, and are verified by numerical simulations. A lower bound on the time-varying step-size is derived to maintain the optimal steady-state performance in nonstationary scenarios. A new method for the estimation of the noise variance is also proposed. Numerical simulations demonstrate the excellent learning performance of the proposed algorithms in comparison with benchmark algorithms.
AB - Sensor networks are an indispensable part of the Internet of Things (IoT), where sensors perform data acquisition and information processing tasks to obtain the parameters of interest so that IoT-based monitoring, diagnosis and other systems respond quickly to the changing conditions, instantaneous faults, etc. Distributed estimation algorithms are usually employed to estimate the parameters of interest in these IoT-based applications. However, when sensor networks have highly correlated input signals and nonstationary behavior in which the parameters of interest are time-varying, conventional distributed estimation algorithms suffer from severely degraded learning performance due to the large eigenvalue spread in the covariance matrix of the input signals and the random perturbation of the parameters of interest. To address these problems, this paper proposes two diffusion Bayesian subband adaptive filter (DBSAF) algorithms from a Bayesian learning perspective. As the highly-correlated input signal is whitened in a multiband structure and an estimate of the uncertainty in the parameters of interest is obtained by performing Bayesian inference, the proposed DBSAF algorithms are able to achieve better learning performance in comparison with the competing diffusion algorithms. The transient and steady-state mean square error performance of the proposed DBSAF algorithms are analyzed, and are verified by numerical simulations. A lower bound on the time-varying step-size is derived to maintain the optimal steady-state performance in nonstationary scenarios. A new method for the estimation of the noise variance is also proposed. Numerical simulations demonstrate the excellent learning performance of the proposed algorithms in comparison with benchmark algorithms.
KW - Adaptive signal processing
KW - distributed estimation algorithm
KW - IoT
KW - multiband structure
KW - sensor networks
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U2 - 10.1109/TCOMM.2021.3100624
DO - 10.1109/TCOMM.2021.3100624
M3 - Article
AN - SCOPUS:85112611978
SN - 0090-6778
VL - 69
SP - 6909
EP - 6925
JO - IEEE Transactions on Communications
JF - IEEE Transactions on Communications
IS - 10
ER -