2021 Fiscal Year Research-status Report
Radio Resource Management in 5G and Beyond Networks: A Layered In-network Learning Approach
| Project/Area Number |
20K11764
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| Research Institution | Ibaraki University |
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Principal Investigator |
王 瀟岩 茨城大学, 理工学研究科(工学野), 准教授 (10725667)
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| Co-Investigator(Kenkyū-buntansha) |
梅比良 正弘 茨城大学, 理工学研究科(工学野), 特命研究員 (00436239)
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Keywords | wireless access / reinforcement learning / federated learning |
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| Outline of Annual Research Achievements |
In FY2021, based on our previous results, we continued our research on achieving global optimization at low bandwidth consumption by integrating federated learning technique. To deal with the global non-independent and identically distributed environmental dataset and constrained bandwidth resources, we propose to integrate federated learning-based edge server upon intelligent BSs, to form a layered in-network learning framework. We compared the performance of the proposed approach with centralized optimal results, in terms of spectrum efficiency and bandwidth consumption through simulations.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
We consider that the research progresses smoothly. We have published 2 journal papers, 2 internal conference papers and multiple domestic conference papers in FY2021 under the support of this funding.
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| Strategy for Future Research Activity |
In the following year, we will consider the problems with sophisticated and practical models. Meanwhile, we will perform the experiments by realizing the proposed scheme in testbeds.
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| Causes of Carryover |
コロナウィルス感染防止のため、多数の国際・国内学会が中止するため、残額が生じてしまう。残りの助成金は２０２２年度の学会の参加費と学術論文の登録費として使用する予定である。
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