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2017 Fiscal Year Annual Research Report

Study of Joint Optimization of Quality of Service/Experience and Security for Differentiated Services in 5G Heterogeneous Networks

Research Project

Project/Area Number 16H05858
Research InstitutionTohoku University

Principal Investigator

Fadlullah Zubair  東北大学, 情報科学研究科, 准教授 (40614011)

Project Period (FY) 2016-04-01 – 2019-03-31
KeywordsQoS / QoE / Security / 5G / IoT / ネットワーク / wireless network / security
Outline of Annual Research Achievements

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.
In this fiscal year, we examined the algorithm from multiple approaches and improved the proposed method.

Current Status of Research Progress
Current Status of Research Progress

2: Research has progressed on the whole more than it was originally planned.

Reason

As with last year, 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.

Strategy for Future Research Activity

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.

  • Research Products

    (8 results)

All 2018 2017

All Journal Article (7 results) (of which Int'l Joint Research: 1 results,  Peer Reviewed: 7 results) Presentation (1 results) (of which Int'l Joint Research: 1 results)

  • [Journal Article] On A Novel Adaptive UAV-Mounted Cloudlet-Aided Recommendation System for LBSNs2018

    • Author(s)
      Fengxiao Tang, Zubair Md. Fadlullah, Bomin Mao, Nei Kato, Fumie Ono, and Ryu Miura
    • Journal Title

      IEEE Transactions on Emerging Topics in Computing

      Volume: - Pages: 1-13

    • DOI

      10.1109/TETC.2018.2792051

    • Peer Reviewed
  • [Journal Article] Characterizing Flow, Application, and User Behavior in Mobile Networks: A Framework for Mobile Big Data2018

    • Author(s)
      Yuanyuan Qiao, Zhizhuang Xing, Zubair Md. Fadlullah, Jie Yang, and Nei Kato
    • Journal Title

      IEEE Wireless Communications

      Volume: 25 Pages: 40-49

    • DOI

      10.1109/MWC.2018.1700186

    • Peer Reviewed / Int'l Joint Research
  • [Journal Article] Multi-Hop Wireless Transmission in Multi-Band WLAN Systems: Proposal and Future Perspective2017

    • Author(s)
      Zubair Md. Fadlullah, Yuichi Kawamoto, Hiroki Nishiyama, Nei Kato, Naoto Egashira, Kazuto Yano, and Tomoaki Kumagai
    • Journal Title

      IEEE Wireless Communications

      Volume: 26 Pages: 108-113

    • DOI

      10.1109/MWC.2017.1700148

    • Peer Reviewed
  • [Journal Article] AC-POCA: Anti-Coordination Game based Partially Overlapping Channels Assignment in Combined UAV and D2D based Networks2017

    • Author(s)
      Fengxiao Tang, Zubair Md. Fadlullah, Nei Kato, Fumie Ono, and Ryu Miura
    • Journal Title

      IEEE Transactions on Vehicular Technology

      Volume: 67 Pages: 1672-1683

    • DOI

      10.1109/TVT.2017.2753280

    • Peer Reviewed
  • [Journal Article] On Removing Routing Protocol from Future Wireless Networks: A Real-time Deep Learning Approach for Intelligent Traffic Control2017

    • Author(s)
      Fengxiao Tang, Bomin Mao, Zubair Md. Fadlullah, Nei Kato, Osamu Akashi, Takeru Inoue, and Kimihiro Mizutani,
    • Journal Title

      IEEE Wireless Communications

      Volume: 25 Pages: 154-160

    • DOI

      10.1109/MWC.2017.1700244

    • Peer Reviewed
  • [Journal Article] Routing or Computing? The Paradigm Shift Towards Intelligent Computer Network Packet Transmission Based on Deep Learning2017

    • Author(s)
      Bomin Mao, Zubair Md. Fadlullah, Fengxiao Tang, Nei Kato, Osamu Akashi, Takeru Inoue, and Kimihiro Mizutani
    • Journal Title

      IEEE Transactions on Computers

      Volume: 66 Pages: 1946-1960

    • DOI

      10.1109/TC.2017.2709742

    • Peer Reviewed
  • [Journal Article] State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems2017

    • Author(s)
      Zubair Md. Fadlullah, Fengxiao Tang, Bomin Mao, Nei Kato, Osamu Akashi, Takeru Inoue, and Kimihiro Mizutani
    • Journal Title

      IEEE Communications Surveys & Tutorials

      Volume: 19 Pages: 2432-2455

    • DOI

      10.1109/COMST.2017.2707140

    • Peer Reviewed
  • [Presentation] A Tensor Based Deep Learning Technique for Intelligent Packet Routing2017

    • Author(s)
      Bomin Mao, Zubair Md. Fadlullah, Fengxiao Tang, Nei Kato, Osamu Akashi, Takeru Inoue, and Kimihiro Mizutani
    • Organizer
      IEEE Global Communications Conference (GLOBECOM 2017)
    • Int'l Joint Research

URL: 

Published: 2019-12-27  

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