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Diffusion least-mean-square (LMS) is a method to estimate and track an unknown parameter at multiple nodes in a network. When the unknown vector has sparsity, the sparse promoting version of diffusion LMS, which utilizes a sparse regularization term in the cost function, is known to show better convergence performance than that of the original diffusion LMS. This paper proposes a novel choice of the coefficients involved in the updates of sparse diffusion LMS using the idea of message propagation. Moreover, we optimize the proposed coefficients with respect to mean-square-deviation at the steady-state. Simulation results demonstrate that the proposed method outperforms conventional methods in terms of the convergence performance.

- Publication
- IEICE TRANSACTIONS on Communications Vol.E104-B No.2 pp.141-148

- Publication Date
- 2021/02/01

- Publicized
- 2020/08/06

- Online ISSN
- 1745-1345

- DOI
- 10.1587/transcom.2020EBT0001

- Type of Manuscript
- PAPER

- Category
- Fundamental Theories for Communications

Ayano NAKAI-KASAI

Kyoto University

Kazunori HAYASHI

Kyoto University

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Ayano NAKAI-KASAI, Kazunori HAYASHI, "An Acceleration Method of Sparse Diffusion LMS based on Message Propagation" in IEICE TRANSACTIONS on Communications,
vol. E104-B, no. 2, pp. 141-148, February 2021, doi: 10.1587/transcom.2020EBT0001.

Abstract: Diffusion least-mean-square (LMS) is a method to estimate and track an unknown parameter at multiple nodes in a network. When the unknown vector has sparsity, the sparse promoting version of diffusion LMS, which utilizes a sparse regularization term in the cost function, is known to show better convergence performance than that of the original diffusion LMS. This paper proposes a novel choice of the coefficients involved in the updates of sparse diffusion LMS using the idea of message propagation. Moreover, we optimize the proposed coefficients with respect to mean-square-deviation at the steady-state. Simulation results demonstrate that the proposed method outperforms conventional methods in terms of the convergence performance.

URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2020EBT0001/_p

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@ARTICLE{e104-b_2_141,

author={Ayano NAKAI-KASAI, Kazunori HAYASHI, },

journal={IEICE TRANSACTIONS on Communications},

title={An Acceleration Method of Sparse Diffusion LMS based on Message Propagation},

year={2021},

volume={E104-B},

number={2},

pages={141-148},

abstract={Diffusion least-mean-square (LMS) is a method to estimate and track an unknown parameter at multiple nodes in a network. When the unknown vector has sparsity, the sparse promoting version of diffusion LMS, which utilizes a sparse regularization term in the cost function, is known to show better convergence performance than that of the original diffusion LMS. This paper proposes a novel choice of the coefficients involved in the updates of sparse diffusion LMS using the idea of message propagation. Moreover, we optimize the proposed coefficients with respect to mean-square-deviation at the steady-state. Simulation results demonstrate that the proposed method outperforms conventional methods in terms of the convergence performance.},

keywords={},

doi={10.1587/transcom.2020EBT0001},

ISSN={1745-1345},

month={February},}

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TY - JOUR

TI - An Acceleration Method of Sparse Diffusion LMS based on Message Propagation

T2 - IEICE TRANSACTIONS on Communications

SP - 141

EP - 148

AU - Ayano NAKAI-KASAI

AU - Kazunori HAYASHI

PY - 2021

DO - 10.1587/transcom.2020EBT0001

JO - IEICE TRANSACTIONS on Communications

SN - 1745-1345

VL - E104-B

IS - 2

JA - IEICE TRANSACTIONS on Communications

Y1 - February 2021

AB - Diffusion least-mean-square (LMS) is a method to estimate and track an unknown parameter at multiple nodes in a network. When the unknown vector has sparsity, the sparse promoting version of diffusion LMS, which utilizes a sparse regularization term in the cost function, is known to show better convergence performance than that of the original diffusion LMS. This paper proposes a novel choice of the coefficients involved in the updates of sparse diffusion LMS using the idea of message propagation. Moreover, we optimize the proposed coefficients with respect to mean-square-deviation at the steady-state. Simulation results demonstrate that the proposed method outperforms conventional methods in terms of the convergence performance.

ER -