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Spatial attention and quantization-based contrastive learning framework for mmWave massive MIMO beam training
http://hdl.handle.net/10061/0002000476
http://hdl.handle.net/10061/0002000476b4edb8bb-61dd-4ba3-9f4b-66baa4cd497b
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||||
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| 公開日 | 2024-06-19 | |||||||||||
| タイトル | ||||||||||||
| タイトル | Spatial attention and quantization-based contrastive learning framework for mmWave massive MIMO beam training | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | MmWave | |||||||||||
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| 主題Scheme | Other | |||||||||||
| 主題 | Massive MIMO | |||||||||||
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| 主題Scheme | Other | |||||||||||
| 主題 | Deep learning | |||||||||||
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| 主題Scheme | Other | |||||||||||
| 主題 | Spatial attention | |||||||||||
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| 主題Scheme | Other | |||||||||||
| 主題 | Feature quantization | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | Contrastive learning | |||||||||||
| 資源タイプ | ||||||||||||
| 資源タイプ | journal article | |||||||||||
| アクセス権 | ||||||||||||
| アクセス権 | open access | |||||||||||
| 著者 |
Jia, Haohui
× Jia, Haohui
× Chen, Na× Urakami, Taisei
× Gao, Hui
× 岡田, 実 |
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| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | Deep learning (DL)-based beam training schemes have been exploited to improve spectral efficiency with fast optimal beam selection for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. To achieve high prediction accuracy, these DL models rely on training with a tremendous amount of labeled environmental measurements, such as mmWave channel state information (CSI). However, demanding a large volume of ground truth labels for beam training is inefficient and infeasible due to the high labeling cost and the requirement for expertise in practical mmWave massive MIMO systems. Meanwhile, a complex environment incurs critical performance degradation in the continuous output of beam training. In this paper, we propose a novel contrastive learning framework, named self-enhanced quantized phase-based transformer network (SE-QPTNet), for reliable beam training with only a small fraction of the labeled CSI dataset. We first develop a quantized phase-based transformer network (QPTNet) with a hierarchical structure to explore the essential features from frequency and spatial views and quantize the environmental components with a latent beam codebook to achieve robust representation. Next, we design the SE-QPTNet including self-enhanced pre-training and supervised beam training. SE-QPTNet pre-trains by the contrastive information of the target user and others with the unlabeled CSI, and then, it is utilized as the initialization to fine-tune with a reduced volume of labeled CSI. Finally, the experimental results show that the proposed framework improves beam prediction accuracy and data rates with 5% labeled data compared to existing solutions. Our proposed framework further enhances flexibility and breaks the limitation of the quantity of label information for practical beam training. | |||||||||||
| 書誌情報 |
en : EURASIP Journal on Wireless Communications and Networking 巻 2023, 号 1, 発行日 2023-07-25 |
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| 出版者 | ||||||||||||
| 出版者 | SpringerOpen | |||||||||||
| ISSN | ||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||
| 収録物識別子 | 1687-1499 | |||||||||||
| 出版者版DOI | ||||||||||||
| 関連タイプ | isReplacedBy | |||||||||||
| 識別子タイプ | DOI | |||||||||||
| 関連識別子 | https://doi.org/10.1186/s13638-023-02277-w | |||||||||||
| 出版者版URI | ||||||||||||
| 関連タイプ | isReplacedBy | |||||||||||
| 識別子タイプ | URI | |||||||||||
| 関連識別子 | https://jwcn-eurasipjournals.springeropen.com/articles/10.1186/s13638-023-02277-w | |||||||||||
| 権利 | ||||||||||||
| 権利情報Resource | http://creativecommons.org/licenses/by/4.0/ | |||||||||||
| 権利情報 | $00A9 The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | |||||||||||
| 著者版フラグ | ||||||||||||
| 出版タイプ | NA | |||||||||||
