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

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Abstract

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.

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Author information

Authors and Affiliations

  1. College of Artificial Intelligence, Southwest University, 400715, Chongqing, China

    Feng Chen, Shuwei Deng, Yi Hua, Shukai Duan & Jiagui Wu

  2. College of Electronic and Information Engineering, Southwest University, 400715, Chongqing, China

    Feng Chen, Shuwei Deng, Yi Hua & Lidan Wang

  3. Brain-Inspired Computing and Intelligent Control Key Laboratory, Southwest University, 400715, Chongqing, China

    Feng Chen, Shuwei Deng & Yi Hua

  4. Chongqing Collaborative Innovation Center for Brain Science, 400715, Chongqing, China

    Feng Chen, Shuwei Deng & Yi Hua

Authors
  1. Feng Chen
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  2. Shuwei Deng
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  3. Yi Hua
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  4. Shukai Duan
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  5. Lidan Wang
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Correspondence to Shukai Duan.

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This work was supported in part by National Key R&D Program of China (Nos. 2018YFB1306600, 2018YFB1306604), the Fundamental Research Funds for the Central Universities (XDJK2020B034).

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Chen, F., Deng, S., Hua, Y. et al. Communication-Reducing Algorithm of Distributed Least Mean Square Algorithm with Neighbor-Partial Diffusion. Circuits Syst Signal Process 39, 4416–4435 (2020). https://doi.org/10.1007/s00034-020-01374-1

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  • DOI: https://doi.org/10.1007/s00034-020-01374-1

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