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