| アイテムタイプ |
学術雑誌論文 = Journal Article(1) |
| 公開日 |
2024-04-18 |
| 資源タイプ |
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|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| タイトル |
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|
タイトル |
Applying Deep Reinforcement Learning for Self-organizing Network Architecture |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 著者 |
Tu, Yi-Hao
Ma, Yi-Wei
Li, Zhi-Xiang
Chen, Jiann-Liang
塚本, 和也
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| 抄録 |
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内容記述タイプ |
Abstract |
|
内容記述 |
Inefficient resource allocation and unstable connection quality for mobile devices are the primary challenges of Self-Organizing Networks (SON). Frequent handovers between base stations result in a network burden imbalance. In contrast, unstable connection quality causes disconnection or signal interference between mobile devices and base stations, influencing network performance and reliability. In recent years, wireless communication technology has extensively used Reinforcement Learning (RL) to obtain the optimal strategy through continuous interaction between agents and their environments. Deep Reinforcement Learning (DRL) is based on Deep Neural Networks (DNN) to handle increasingly complex network situations. We proposed a SON architecture based on DRL in response to the aforementioned challenges. We described how the agent learns the optimal parameter settings through training based on various network scenarios to develop handover strategies and enhance overall network performance and resource utilization. The proposed framework can be applied to the present Fifth Generation (5G) network. |
|
言語 |
en |
| 備考 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
6th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2023, 11-13 August 2023, Sapporo, Japan |
|
言語 |
en |
| 書誌情報 |
en : 2023 IEEE 6th International Conference on Knowledge Innovation and Invention (ICKII)
p. 16-20,
発行日 2023-12-04
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| 出版社 |
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出版者 |
IEEE |
| DOI |
|
|
関連タイプ |
isVersionOf |
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|
識別子タイプ |
DOI |
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|
関連識別子 |
https://doi.org/10.1109/ICKII58656.2023.10332666 |
| ISSN |
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収録物識別子タイプ |
PISSN |
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収録物識別子 |
2770-4777 |
| ISSN |
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収録物識別子タイプ |
EISSN |
|
収録物識別子 |
2770-4785 |
| 著作権関連情報 |
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|
権利情報 |
Copyright (c) 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
deep reinforcement learning |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
handover optimization |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
mobility load balancing |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
mobility robustness optimization |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
self-organizing networks |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| 査読の有無 |
|
|
値 |
yes |
| 研究者情報 |
|
|
URL |
https://hyokadb02.jimu.kyutech.ac.jp/html/218_ja.html |
| 論文ID（連携） |
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|
値 |
10429332 |
| 連携ID |
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|
値 |
11999 |
10429332.pdf (1.6 MB)
