強化学習を用いた広帯域ミリ波最適スペクトル制御と先験知識を予測する技術の開発
| Project/Area Number |
20J12528
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Single-year Grants |
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| Section | 国内 |
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| Review Section |
Basic Section 60010:Theory of informatics-related
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| Research Institution | Keio University |
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Principal Investigator |
曹 誉文 慶應義塾大学, 理工学部, 特別研究員(PD)
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| Project Period (FY) |
2020-04-24 – 2022-03-31
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| Project Status |
Discontinued (Fiscal Year 2021)
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| Budget Amount *help |
¥1,700,000 (Direct Cost: ¥1,700,000)
Fiscal Year 2021: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2020: ¥900,000 (Direct Cost: ¥900,000)
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| Keywords | Deep learning / Resource allocation / Network control / Mobile edge computing / Analog beamforming / mmWave communications / deep learning / routing / caching / dense networks / federated learning |
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| Outline of Research at the Start |
This research focuses on developing innovative spectrum resource allocation and electromagnetic radiation pattern predicting algorithms for 28 GHz mmWave channels. This research will find new technologies that can be utilized in mmWave or Terahertz band communications, and tackle some open problems.
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| Outline of Annual Research Achievements |
In this research-year, I focuse mainly on the following two research-topics: 1>. Low-overhead beam and power resource allocation using deep learning and its application in multiuser millimeter-wave (mmWave) communications; 2>. Deep learning-based network control and management in mobile edge computing (MEC) systems. Related research results have been published in high-quality journal and conference papers, respectively. Throughout our experiments, I generate images of resolution 4×4 and 8×8 and use these for distance estimation between users. Afterwards, I apply super resolution on images with size 4×4 to improve their resolution, and compare their results to the ones obtained with the original 8×8 images. For an area roughly equal to 60×30m, the proposed approach reaches an average mean squared error equal to 0.13 m.
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| Research Progress Status |
令和3年度が最終年度であるため、記入しない。
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| Strategy for Future Research Activity |
令和3年度が最終年度であるため、記入しない。
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Report
(2 results)
Research Products
(13 results)
