Paper Abstract and Keywords |
Presentation |
2021-07-14 10:30
Deep-Unfolding Aided Optimization of Edge Weights and Step Sizes for Diffusion LMS Algorithm Yuto Nishihata, Koji Ishii (Kagawa Univ.) RCC2021-22 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
This study proposes a deep-unfolding aided parameter setting for a diffusion LMS algorithm. Distributed signal processing can avoid the centralization of huge computational burden and/or power consumption at some agents in the network, while its convergence performance of signal processing becomes worse than the one of centralized signal processing. To accelerate the convergence performance, this study applies a deep unfolding to the focused diffusion LMS algorithm. Specifically, this work tries to optimize both step size and weighted adjacent matrix in the diffusion LMS algorithm. Simulation results show that the diffusion LMS with the optimized parameters can significantly enhance the convergence performance compared to the case with fixed parameters. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Diffusion LMS Algorithm / deep-learning / deep-unfolding / average consensus / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 101, RCC2021-22, pp. 1-6, July 2021. |
Paper # |
RCC2021-22 |
Date of Issue |
2021-07-07 (RCC) |
ISSN |
Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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RCC2021-22 |
Conference Information |
Committee |
RCS SR NS SeMI RCC |
Conference Date |
2021-07-14 - 2021-07-16 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Communication and Network Technology of the AI Age, M2M (Machine-to-Machine),D2D (Device-to-Device),IoT(Internet of Things), etc |
Paper Information |
Registration To |
RCC |
Conference Code |
2021-07-RCS-SR-NS-SeMI-RCC |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Deep-Unfolding Aided Optimization of Edge Weights and Step Sizes for Diffusion LMS Algorithm |
Sub Title (in English) |
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Diffusion LMS Algorithm |
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deep-learning |
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deep-unfolding |
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average consensus |
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1st Author's Name |
Yuto Nishihata |
1st Author's Affiliation |
Kagawa University (Kagawa Univ.) |
2nd Author's Name |
Koji Ishii |
2nd Author's Affiliation |
Kagawa University (Kagawa Univ.) |
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Speaker |
Author-1 |
Date Time |
2021-07-14 10:30:00 |
Presentation Time |
25 minutes |
Registration for |
RCC |
Paper # |
RCC2021-22 |
Volume (vol) |
vol.121 |
Number (no) |
no.101 |
Page |
pp.1-6 |
#Pages |
6 |
Date of Issue |
2021-07-07 (RCC) |
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