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Radio Resource Management in 5G and Beyond Networks: A Layered In-network Learning Approach

Research Project

Project/Area Number 20K11764
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeMulti-year Fund
Section一般
Review Section Basic Section 60060:Information network-related
Research InstitutionIbaraki University

Principal Investigator

Wang Xiaoyan  茨城大学, 理工学研究科(工学野), 准教授 (10725667)

Co-Investigator(Kenkyū-buntansha) 梅比良 正弘  茨城大学, 理工学研究科(工学野), 特命研究員 (00436239)
Project Period (FY) 2020-04-01 – 2024-03-31
Project Status Completed (Fiscal Year 2023)
Budget Amount *help
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2022: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2021: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2020: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Keywords周波数活用 / wireless access / federated learning / reinforcement learning / In-network learning / radio resource
Outline of Research at the Start

Radio resource management (RRM) is the key enabler for full-featured 5G networks. In this research, we propose a layered in-networking learning RRM approach, and evaluate its performance via both simulations and experiments on testbeds.

Outline of Final Research Achievements

The 5G system adopts a small cell configuration, which significantly improves frequency efficiency but also leads to inter-cell interference issues. To address this problem, beamforming, a technique that focuses radio waves in specific directions, is widely considered. Achieving globally optimal beamforming control in multi-cell systems is highly challenging. This study aims to maximize the energy efficiency of the entire network in both static and dynamic downlink scenarios, proposing a low-resolution analog beamforming and transmission power control method using deep reinforcement learning.

Academic Significance and Societal Importance of the Research Achievements

本研究では、無線リソースの最適化の実現に向け、深層強化学習を用いたアナログビームフォーマと送信電力制御について検討する。提案手法は従来手法と比べ、静的および動的なシナリオにおける、大幅なエネルギー効率改善が期待でき、周波数資源の有効利用に向けて重要な役割を持っていると考えられる。

Report

(5 results)
  • 2023 Annual Research Report   Final Research Report ( PDF )
  • 2022 Research-status Report
  • 2021 Research-status Report
  • 2020 Research-status Report
  • Research Products

    (11 results)

All 2022 2021 2020

All Journal Article (6 results) (of which Int'l Joint Research: 5 results,  Peer Reviewed: 6 results,  Open Access: 3 results) Presentation (5 results) (of which Int'l Joint Research: 5 results)

  • [Journal Article] Green Spectrum Sharing Framework in B5G Era by Exploiting Crowdsensing2022

    • Author(s)
      Xiaoyan Wang, Masahiro Umehira, Mina Akimoto, Biao Han and Hao Zhou
    • Journal Title

      IEEE Transactions on Green Communications and Networking

      Volume: 22(18) Issue: 2 Pages: 1-16

    • DOI

      10.1109/tgcn.2022.3186282

    • Related Report
      2022 Research-status Report
    • Peer Reviewed / Int'l Joint Research
  • [Journal Article] A Usage Aware Dynamic Spectrum Access Scheme by Exploiting Deep Reinforcement Learning2022

    • Author(s)
      Xiaoyan Wang, Yuto Teraki, Masahiro Umehira, Hao Zhou and Yusheng Ji
    • Journal Title

      Sensors

      Volume: 1 Issue: 18 Pages: 1-12

    • DOI

      10.3390/s22186949

    • Related Report
      2022 Research-status Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Journal Article] Asynchronous Federated Deep Reinforcement Learning-Based URLLC-Aware Computation Offloading in Space-Assisted Vehicular Networks2022

    • Author(s)
      Chao Pan, Zhao Wang, Haijun Liao, Zhenyu Zhou, Xiaoyan Wang, Muhammad Tariq, and Sattam Al-Otaibi
    • Journal Title

      IEEE Transactions on Intelligent Transportation Systems

      Volume: early access Issue: 7 Pages: 1-13

    • DOI

      10.1109/tits.2022.3150756

    • Related Report
      2021 Research-status Report
    • Peer Reviewed / Int'l Joint Research
  • [Journal Article] Reinforcement Learning for Joint Channel/Subframe Selection of LTE in the Unlicensed Spectrum2021

    • Author(s)
      Yuki Kishimoto, Xiaoyan Wang, and Masahiro Umehira
    • Journal Title

      Wireless Communications and Mobile Computing

      Volume: 2021 Issue: 1 Pages: 1-15

    • DOI

      10.1155/2021/9985972

    • Related Report
      2021 Research-status Report
    • Peer Reviewed / Open Access
  • [Journal Article] Wireless Access Control in Edge-Aided Disaster Response: A Deep Reinforcement Learning-based Approach2021

    • Author(s)
      Hang Zhou, Xiaoyan Wang*, Masahiro Umehira, Xianfu Chen, Celimuge Wu, and Yusheng Ji
    • Journal Title

      IEEE Access

      Volume: 9 Pages: 46600-46611

    • DOI

      10.1109/access.2021.3067662

    • Related Report
      2020 Research-status Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Journal Article] When Vehicular Fog Computing Meets Autonomous Driving: Computational Resource Management and Task Offloading2020

    • Author(s)
      Zhenyu Zhou, Haijun Liao, Xiaoyan Wang*, Shahid Mumtaz, Jonathan Rodriguez
    • Journal Title

      IEEE Network

      Volume: 34 Issue: 6 Pages: 70-76

    • DOI

      10.1109/mnet.001.1900527

    • Related Report
      2020 Research-status Report
    • Peer Reviewed / Int'l Joint Research
  • [Presentation] Deep Reinforcement Learning based Secondary User Transmit Power Control for Underlay Cognitive Radio Networks2022

    • Author(s)
      Kouhei Kato, Xiaoyan Wang, Masahiro Umehira and Yusheng Ji
    • Organizer
      ACM Research in Adaptive and Convergent Systems
    • Related Report
      2022 Research-status Report
    • Int'l Joint Research
  • [Presentation] A Deep Reinforcement Learning based Analog Beamforming Approach in Downlink MISO Systems2022

    • Author(s)
      Hang Zhou, Xiaoyan Wang, Masahiro Umehira, and Yusheng Ji
    • Organizer
      IEEE Vehicular Technology Conference
    • Related Report
      2022 Research-status Report 2021 Research-status Report
    • Int'l Joint Research
  • [Presentation] Deep Reinforcement Learning based Usage Aware Spectrum Access Scheme2021

    • Author(s)
      Yuto Teraki, Xiaoyan Wang, Masahiro Umehira and Yusheng Ji
    • Organizer
      nternational Symposium on Wireless Personal Multimedia Communications
    • Related Report
      2021 Research-status Report
    • Int'l Joint Research
  • [Presentation] Reinforcement Learning based Joint Channel/Subframe Selection Scheme for Fair LTE-WiFi Coexistence2020

    • Author(s)
      Yuki Kishimoto, Xiaoyan Wang and Masahiro Umehira
    • Organizer
      IEEE MSN
    • Related Report
      2020 Research-status Report
    • Int'l Joint Research
  • [Presentation] Deep Reinforcement Learning based Access Control for Disaster Response Networks2020

    • Author(s)
      Hang Zhou, Xiaoyan Wang, Masahiro Umehira, Xianfu Chen, Celimuge Wu, Yusheng Ji
    • Organizer
      IEEE Globecom
    • Related Report
      2020 Research-status Report
    • Int'l Joint Research

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Published: 2020-04-28   Modified: 2025-01-30  

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