Communication-Reducing Algorithm of Distributed Least Mean Square Algorithm with Neighbor-Partial Diffusion

With the development of distributed algorithms, many researchers are committed to the goal of maintaining the long-term stability of the network by reducing the communication cost. However, many algorithms that lessen communication costs often result in a significant decrease in estimation accuracy....

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Bibliographic Details
Published inCircuits, systems, and signal processing Vol. 39; no. 9; pp. 4416 - 4435
Main Authors Chen, Feng, Deng, Shuwei, Hua, Yi, Duan, Shukai, Wang, Lidan, Wu, Jiagui
Format Journal Article
LanguageEnglish
Published New York Springer US 01.09.2020
Springer Nature B.V
Subjects
Algorithms
Circuits and Systems
Communication
Cost analysis
Electrical Engineering
Electronics and Microelectronics
Engineering
Instrumentation
Performance degradation
Redundancy
Signal,Image and Speech Processing
Stability analysis
Wireless sensor networks
Distributed estimation
Neighbor-partial
Diffusion
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Summary:With the development of distributed algorithms, many researchers are committed to the goal of maintaining the long-term stability of the network by reducing the communication cost. However, many algorithms that lessen communication costs often result in a significant decrease in estimation accuracy. In order to reduce the communication cost with less performance degradation, the distributed neighbor-partial diffusion least-mean-square algorithm (NPDLMS) is proposed in this paper. Besides, considering the data redundancy in the network, we offer the distributed data selection NPDLMS algorithm, which further improves the estimation accuracy and reduces the communication cost. In the performance analysis, the stability and the communication cost of the algorithms are given.
ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-020-01374-1