Wireless Communications using Signal Processing Design based on Conditional Mutual Information Norm Adaptive Quantization and Deep Learning

2022 Fiscal Year Final Research Report

• Project/Area Number: 19H02142
• Research Category: Grant-in-Aid for Scientific Research (B)
• Allocation Type: Single-year Grants
• Section: 一般
• Review Section: Basic Section 21020:Communication and network engineering-related
• Research Institution: Keio University
• Principal Investigator: Otsuki Tomoaki 慶應義塾大学, 理工学部(矢上), 教授 (10277288)
• 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)
• Project Period (FY): 2019-04-01 – 2023-03-31
• Keywords: MIMO / CSI / 深層学習 / 第6世代無線通信システム

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.

Free Research Field

無線通信工学

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．