Sensing Augmented Database-Driven Spectrum Sharing Framework
| Project/Area Number |
17K12670
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Information network
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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), 助教 (10725667)
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Project Status |
Granted (Fiscal Year 2018)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2019: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2018: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2017: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Keywords | spectrum sharing / incentive mechanism / Spectrum sharing |
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| Outline of Annual Research Achievements |
Based on our research results in 2017, we studied crowdsourced radio environment map construction problem by using machine learning techniques, and online crowdsourcing incentive mechanism for radio environment map by taking into consideration the dynamically arrived users. Specifically, we proposed two machine learning based approaches to select the measurement, i.e., one decision tree based approach and one neural network based approach. For the second problem, we propose a novel online incentive mechanism for sensing augmented spectrum database. Simulation results demonstrate that the proposed mechanism could save noticeable expenditure.
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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 1 journal paper, 2 internal conference papers and multiple domestic conference papers in the
past year under this funding. The 2 research problems considered in the application form are all well addressed.
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| Strategy for Future Research Activity |
In the following year, we will extend the problems to sophisticated and practical ones (i.e., machine learning based zone refinement and resource reward incentive mechanism). Meanwhile, we will perform the experiments on real radio environment map construction by using spectrum analyzer.
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Report
(2 results)
Research Products
(8 results)
