2021 Fiscal Year Annual Research Report
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
|
|---|
| Research Institution | Keio University |
|---|
Principal Investigator |
曹 誉文 慶應義塾大学, 理工学部, 特別研究員(PD)
|
|---|
| Project Period (FY) |
2020-04-24 – 2022-03-31
|
| Keywords | Deep learning / Resource allocation / Network control / Mobile edge computing |
|---|
| 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.
|
| Research Progress Status |
令和3年度が最終年度であるため、記入しない。
|
| 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。
|
