| 研究課題/領域番号 |
16H05858
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| 研究機関 | 東北大学 |
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研究代表者 |
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| 研究期間 (年度) |
2016-04-01 – 2019-03-31
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| キーワード | 5G / wireless network / security / optimization |
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| 研究実績の概要 |
I used game theory to address the tradeoff of QoS and security in the next generation wireless networks. Particularly for the 4G and beyond 4G networks, the tradeoff issue between QoS and security in terms of throughput, delay, encryption/decryption, authentication, and so forth were taken into consideration. The problem was formally constructed using a game-based model by arguing that a model couldn't be constructed to trivially apply conventional optimization methods. Both mobility and fixed scenarios were considered and simulations were conducted showing the effectiveness of the game based method. We then extended our research to other next generation network such as IoT where QoS and security optimization is also critical.
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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
So far, I have used game theory to integrate security and QoS. However, from the recent breakthrough in deep learning, I am studying how to improve the network QoS aspect more by training using large network datasets with the aid of deep learning techniques.
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| 今後の研究の推進方策 |
Now I am studying the 5G network architecture. In the future, particular focus will be given on ultra dense network or UDN where beamforming and massive MIMO technologies are used. Using traditional FDD or TDD, such technologies are prone to resource allocation problems. I am planning to using deep learning, which is the state-of-the-art machine learning / Artificial Intelligence (AI) technique, to plan for intelligent resource control for mobile users in 5G UDNs to alleviate potential congestion and support next generation services and applications. After the resource allocation based QoS and QoE implementation, security integration focus with deep learning will also be taken into account.
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