An Acceleration Method of Sparse Diffusion LMS based on Message Propagation
Summary :
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
Authors
Ayano NAKAI-KASAI
Kyoto University
Kazunori HAYASHI
Kyoto University
Keyword
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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},}
Copy
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 -
English
