| Presentation | 2021-07-14 Deep-Unfolding Aided Optimization of Edge Weights and Step Sizes for Diffusion LMS Algorithm Yuto Nishihata, Koji Ishii, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(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) |
| Keyword(in English) | Diffusion LMS Algorithm / deep-learning / deep-unfolding / average consensus |
| Paper # | RCC2021-22 |
| Date of Issue | 2021-07-07 (RCC) |
| Conference Information | |
| Committee | RCS / SR / NS / SeMI / RCC |
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| Conference Date | 2021/7/14(3days) |
| 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 |
| Chair | Eiji Okamoto(Nagoya Inst. of Tech.) / Suguru Kameda(Hiroshima Univ.) / Akihiro Nakao(Univ. of Tokyo) / Koji Yamamoto(Kyoto Univ.) / HUAN-BANG LI(NICT) |
| Vice Chair | Toshihiko Nishimura(Hokkaido Univ.) / Tomoya Tandai(Toshiba) / Fumihide Kojima(NICT) / Osamu Takyu(Shinshu Univ.) / Kentaro Ishidu(NICT) / Kazuto Yano(ATR) / Tetsuya Oishi(NTT) / Kazuya Monden(Hitachi) / Yasunori Owada(NICT) / Shunichi Azuma(Nagoya Univ.) / Koji Ishii(Kagawa Univ.) |
| Secretary | Toshihiko Nishimura(NEC) / Tomoya Tandai(Panasonic) / Fumihide Kojima(Mie Univ.) / Osamu Takyu(Tokai Univ.) / Kentaro Ishidu(NTT) / Kazuto Yano(NTT) / Tetsuya Oishi(Chuo Univ.) / Kazuya Monden(Cyber Univ.) / Yasunori Owada(Waseda Univ.) / Shunichi Azuma(Osaka Univ.) / Koji Ishii(CRIEPI) |
| Assistant | Koichi Adachi(Univ. of Electro-Comm.) / Osamu Nakamura(Sharp) / Manabu Sakai(Mitsubishi Electric) / Masashi Iwabuchi(NTT) / Tatsuki Okuyama(NTT DOCOMO) / Mai Ohta(Fukuoka Univ.) / Taichi Ohtsuji(NEC) / WANG Xiaoyan(Ibaraki Univ.) / Akemi Tanaka(MathWorks) / Kotaro Mihara(NTT) / Yuki Katsumata(NTT DOCOMO) / Akihito Taya(Aoyama Gakuin Univ.) / Yu Nakayama(Tokyo Univ. of Agri. and Tech.) / SHAN LIN(NICT) / Masaki Ogura(Osaka Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Radio Communication Systems / Technical Committee on Smart Radio / Technical Committee on Network Systems / Technical Committee on Sensor Network and Mobile Intelligence / Technical Committee on Reliable Communication and Control |
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| Language | JPN |
| 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) | |
| Keyword(1) | Diffusion LMS Algorithm |
| Keyword(2) | deep-learning |
| Keyword(3) | deep-unfolding |
| Keyword(4) | average consensus |
| 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.) |
| Date | 2021-07-14 |
| Paper # | RCC2021-22 |
| Volume (vol) | vol.121 |
| Number (no) | RCC-101 |
| Page | pp.pp.1-6(RCC), |
| #Pages | 6 |
| Date of Issue | 2021-07-07 (RCC) |