| 研究課題/領域番号 |
22K14263
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| 研究機関 | 電気通信大学 |
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研究代表者 |
李 傲寒 電気通信大学, 大学院情報理工学研究科, 助教 (50876810)
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| 研究期間 (年度) |
2022-04-01 – 2025-03-31
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| キーワード | Intelligent 6G / AI based optimization / Spectrum efficiency / Energy efficiency |
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| 研究実績の概要 |
1) Automatics communication parameter decision in a practical uncertain wireless environment. It is verified that spectrum efficiency can be improved by considering more transmission parameters, such as transmission interval.
2) AI algorithms to improve energy efficiency. A lightweight AI algorithm has been proposed to improve the energy efficiency while an experimental platform for verifying the performance of the proposed AI algorithm in energy efficiency is built.
3) A Quantum-annealing-based ultrafast optimization algorithm is proposed for optimizing the multiplexed techniques-based network performance.
Based on the research results, two journal papers have been published, and more than three reports have been presented or will be presented in international conferences/workshops.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
To achieve the goals of this project. I aimed to research the first and the second questions and make preliminary preparations for the third questions. That is, [Q1] Joint dynamic spectrum access and multiplexing techniques for optimizing the spectrum and energy efficiency. [Q2] Automatic communication parameter decision based on AI in a practical uncertain wireless environment. [Q3] Development of lightweight AI algorithms to improve energy efficiency.
Moreover, at least two journal papers and two international conference papers are expected to be submitted. Regarding [Q1] and [Q2], two journal papers have been published, while two journal papers are under revision. Regarding [Q3], the proposed algorithm has been presented at a domestic conference and international workshop.
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| 今後の研究の推進方策 |
1) Compare the energy efficiency by jointly considering the dynamic spectrum access and multiplexing techniques and without a joint consideration to clarify how much energy efficiency can be improved. Moreover, more efficient multiplexing techniques will be considered.
2) Improve the spectrum and energy efficiency of the proposed AI algorithm by considering more transmission parameters.
3) Joint optimization of AI and wireless communications. Specifically, the object optimization problem of the joint optimization of AI and wireless communications will be formulated. Then, quantum annealing-based high-speed optimization algorithms will be proposed to solve the formulated objective optimization problem.
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