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Diffusion Bias-Compensation RLS Estimation Over Noisy Node-Specific Networks

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Abstract

We study the node-specific parameter estimation problem, where agents in a network collaborate to obtain the different but overlapping vectors of parameters, which can be of local interest, common interest to a subset of agents, and global interest to the whole network. We assume that all the regressors and the measurements are corrupted by additive noise. For these settings, a bias-compensation recursive-least-square algorithm based on a diffusion mode of cooperation is proposed; its stability is obtained via the detailed derivation of convergence in the mean sense. In addition, a closed-form expression for the algorithm’s mean-square deviation is also provided to evaluate the steady-state performance of the whole network. Finally, we present simulation results that indicate the efficiency of the proposed method.

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Data Availability Statement

All data included in this study are available upon request by contacting the corresponding author.

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Authors and Affiliations

  1. School of Information and Electronics, Beijing Institute of Technology, Beijing, China

    Chenxue Zheng, Lijuan Jia & Yue Wang

  2. Department of Intelligent Systems Engineering, Ibaraki University, 4-12-1 Nakanarusawa, Hitachi, Ibaraki, 316-8511, Japan

    Zi-Jiang Yang

Authors
  1. Chenxue Zheng
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  2. Lijuan Jia
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  3. Zi-Jiang Yang
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  4. Yue Wang
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Correspondence to Lijuan Jia.

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This work was supported in part by the National Natural Science foundation of China under Grants 41927801.

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Zheng, C., Jia, L., Yang, ZJ. et al. Diffusion Bias-Compensation RLS Estimation Over Noisy Node-Specific Networks. Circuits Syst Signal Process 40, 2564–2583 (2021). https://doi.org/10.1007/s00034-020-01591-8

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  • DOI: https://doi.org/10.1007/s00034-020-01591-8

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