Radio Resource Management in 5G and Beyond Networks: A Layered In-network Learning Approach
| Project/Area Number |
20K11764
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 60060:Information network-related
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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 – 2024-03-31
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| Project Status |
Granted (Fiscal Year 2022)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2022: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2021: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2020: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | wireless access / federated learning / reinforcement learning / In-network learning / radio resource |
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| Outline of Research at the Start |
Radio resource management (RRM) is the key enabler for full-featured 5G networks. In this research, we propose a layered in-networking learning RRM approach, and evaluate its performance via both simulations and experiments on testbeds.
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| Outline of Annual Research Achievements |
In FY2022, we investigate the practical global model update process in wireless networks by proposing a robust asynchronized computing and communication process. Specifically, we proposed to decouple the computing and communication processes, and let the edge server use a subset of asynchronized local gradients to update the global model. We proved the algorithm’s convergence and evaluated its performance by 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 FY2022 under the support of this funding.
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| Strategy for Future Research Activity |
In the following year, we will implement the proposed layered in-network learning approach. Specifically, the main functions of FL will be implemented in the edge server (a GPU-workstation), and meanwhile, global gradient updating features would be added to the intelligent BSs. We will extensively evaluate the performance in terms of network throughput, spectrum utilization, bandwidth consumption and convergence speed, by using our developed testbed.
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Report
(3 results)
Research Products
(11 results)
