2020 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), 教授 (00436239)
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Keywords | wireless access / reinforcement learning |
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| Outline of Annual Research Achievements |
In FY2020, we started from the fundamental workload, i.e., optimizing local radio resource management, by
designing distributed deep reinforcement learning based approach. The intelligence is placed at user equipments, who learn their wireless access decisions by relying only on a local set of observations from the wireless environment, such as channel quality and interference levels. We clarified the tradeoff between
allocated radio resource’s granularity and learning algorithm’s convergence speed by simulations on TensorFlow. We also evaluated the performance of the proposed scheme in terms of transmission delay and packet drop rate by comparing baseline schemes.
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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 FY2020 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２０２１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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