| 研究実績の概要 |
During the past year, I have mainly conducted research on two topics. That is:
1) Joint dynamic spectrum access and multiplexing techniques for optimizing the spectrum and energy efficiency.
2) Automatic communication parameter decision based on AI in a practical uncertain wireless environment.
For the first topic, a quantum annealing-based resource allocation was proposed for dynamic non-orthogonal multiple access (NOMA) systems. Specifically, an optimization objective problem is first formulated, considering the dynamic spectrum access and NOMA. Then, the optimal transmission parameters, including channel and transmission power, were derived through an exhaustive search and quantum annealing methods. Compared with the spectrum efficiency while jointly considering the dynamic spectrum access and NOMA to that without a joint consideration, it is clarified that the spectrum efficiency can be improved.
For the second topic, several AI algorithms were proposed to determine the transmission parameters in a decentralized manner, including the channel, power, and spreading factor. The AI algorithms include laser chaos-based multi-armed bandit, Tug of War dynamics, and deep reinforcement learning methods. In addition, the proposed algorithms were verified by simulation and experiments conducted in a practical uncertain wireless environment.
Based on the research results, 1 journal paper and more than 3 related international papers have been published. In addition, 2 journal papers have been submitted.
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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 solve the first question ([Q1] Joint dynamic spectrum access and multiplexing techniques for optimizing the spectrum and energy efficiency) during the year 2022. Actually, I was working on both the first and the second questions ([Q2] Automatic communication parameter decision based on AI in a practical uncertain wireless environment.) For [Q1], I have verified that spectrum efficiency can be improved by jointly considering dynamic spectrum access and multiplexing techniques (especially for the NOMA technique). In addition, the spectrum and energy efficiency when considering dynamic spectrum access and multiplexing techniques (NOMA and Pulse) are under verification. For [Q2], I have verified that suitable parameters (channel, transmission power, spreading factor) can be decided automatically based on AI in a practical uncertain wireless environment. Moreover, one journal paper and one international conference paper were expected to be submitted based on the research results in the research proposal. Actually, 1 journal paper and more than 3 related international papers have been published. In addition, 2 journal papers have been submitted. As described above, although the order of the research has changed a bit from what was originally expected, the research project is progressing rather smoothly.
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| 今後の研究の推進方策 |
Next year, I will complete the research on the first and the second questions and make preliminary preparations for the third questions. For the first question, I will continue to verify that the spectrum and energy efficiency can be improved by jointly considering dynamic spectrum access and multiplexing techniques (NOMA, Pulse). For the second question, I will jointly consider more transmission parameters, such as cod rate, payload, transmission interval, and so on. In addition, I will further improve the AI algorithm to obtain a higher spectrum and energy efficiency. Moreover, the improved AI techniques will be applied to the system with dynamic spectrum access and multiplexing (NOMA, Pulse) techniques to further improve the spectrum and energy efficiency. For the preliminary preparations of the third question ([Q3] Development of lightweight AI algorithms to improve the computing energy efficiency), I will formulate the problem as the energy efficiency maximization problem and try to propose cooperative learning methods to improve the computing energy efficiency and try to build an experimental platform for verifying the performance of the proposed AI algorithms in computing energy efficiency.
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