| Project/Area Number |
19H02142
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Review Section |
Basic Section 21020:Communication and network engineering-related
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| Research Institution | Keio University |
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Principal Investigator |
Otsuki Tomoaki 慶應義塾大学, 理工学部(矢上), 教授 (10277288)
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| Co-Investigator(Kenkyū-buntansha) |
豊田 健太郎 慶應義塾大学, 理工学部(矢上), 訪問助教 (60723476)
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| Project Period (FY) |
2019-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥17,290,000 (Direct Cost: ¥13,300,000、Indirect Cost: ¥3,990,000)
Fiscal Year 2022: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
Fiscal Year 2021: ¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2020: ¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2019: ¥6,110,000 (Direct Cost: ¥4,700,000、Indirect Cost: ¥1,410,000)
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| Keywords | MIMO / CSI / 深層学習 / 第6世代無線通信システム / 転移学習 / 通信路状態情報 / Massive MIMO / 適応量子化情報 / 条件付き相互情報量 / 変調識別器 / CSIフィードバック / FDD / BPアルゴリズム / Polar符号 / BP復号 / パイロット汚染 / ミリ波 / 超解像技術 |
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| Outline of Research at the Start |
本研究では，無線通信路に適した適応量子化及び量子化情報に基づく信号処理設計と，深層学習に基づくパラメータ最適化・入力推定を用いた超大容量・超低遅延・超低消費電力無線通信の実現を目指す．具体的には，まず統計的性質が異なる種々の状態を取る未知の無線通信路に対する条件付き相互情報量規範の適応量子化法について検討する．
次に，量子化情報に基づく通信路推定・誤り訂正復号器等の各種信号処理設計について検討する．また，深層学習を用いた無線通信システムの各パラメータ最適化・入力推定について研究する．
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| Outline of Final Research Achievements |
We propose a feedback method for CSI, based on autoencoder, trasnfer learning, quantization, and so on. We have proposed several methods to obtain high accuracy with small amount of feedback information, and have been presented at IEEE international conferences, etc. In addition, we proposed a new method of coalition learning that reduces the amount of computation in the device and the amount of communication between the device and the server, which had been a problem in the past. The results were presented at the IEEE International Conference on Wireless Communication Systems, and others. In addition, we proposed a new distance estimation method based on deep learning using Massive MIMO and millimeter wave, which will be key technologies in 5G and Beyond 5G.
We also proposed a new distance estimation method based on deep learning for Massive MIMO and millimeter wave, which are key technologies for 5G and Beyond 5G.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究では，無線通信路に適した適応量子化及び量子化情報に基づく信号処理設計と，深層学習に基づくパラメータ最適化・入力推定を用いた超大容量・超低遅延・超低消費電力無線通信の実現を目指し研究した．複数の方式を開発・提案したが，研究成果に基づき，Beyond 5Gや6Gで要求される大容量化及び処理遅延低減を実現することが期待される．また，コロナに対応する新たなアプリケーションとしても期待される．
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