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Cooperative Beam Management for Cell-Free Multi-Layer Heterogeneous Network

Research Project

Project/Area Number 23K16870
Research Category

Grant-in-Aid for Early-Career Scientists

Allocation TypeMulti-year Fund
Review Section Basic Section 60060:Information network-related
Research InstitutionNara Institute of Science and Technology

Principal Investigator

Chen Na  奈良先端科学技術大学院大学, 先端科学技術研究科, 助教 (80838342)

Project Period (FY) 2023-04-01 – 2026-03-31
Project Status Granted (Fiscal Year 2023)
Budget Amount *help
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2025: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2024: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2023: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
KeywordsBeam Management / Deep Learning / Massive MIMO / IRS / Heterogeneous Network / Cell-Free Communication
Outline of Research at the Start

The B5G/6G systems are confronted with high transmission requirements and a harsh wireless communication environment. This research considers a radio over fiber (RoF) supported multi-layer cell-free heterogeneous network (HetNet) architecture to achieve an efficient transmission in complex scenarios. We first propose the HetNet model and deep learning (DL) method for cooperative beam management considering the nonlinear optic fiber channel and the cell-free wireless channel, providing a possible solution for future wireless communication networks with high throughput and robustness.

Outline of Annual Research Achievements

This research focus on deep learning based heterogeneous network (HetNet) beam management. We aim at developing a deep learning empowered cooperative beam management scheme for the RIS and radio-over-fiber (RoF) and intelligent reflecting surface (IRS) assisted cell-free (CF) HetNet system. We also aim at carrying out a hardware demo with campus local 5G experiment network to confirm the model performance. Comparing with existing study, this is a comprehensive study on deep learning model design for complex communication system beam cooperation with demo implementation, which is of high importance for 5G/6G systems. We mainly did the following study in the past year:
1. We developed deep learning schemes for massive multiple-input multiple-output (mMIMO) beam training. Specifically, we introduced contrastive learning mechanism with Transformer model for reducing the training overhead and improve model efficiency.
2. We developed deep learning algorithms for IRS-assisted mMIMO communication. Specifically, we considered a practical semi-passive IRS design to collect the channel information. Then we designed hybrid convolutional neural network (CNN) encoder-based Transformer deep learning model for IRS beam selection. The model achieved high prediction performance and spectral efficiency.
3. We implemented some hardware demo for RIS. We designed LC-based IRS structure and mushroom style patch design for IRS. Besides, we measured the local 5G channel character and radio-over-fiber channel character.

Current Status of Research Progress
Current Status of Research Progress

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

Reason

Based on the theoretic analysis of the IRS-assisted mMIMO system and algorithm simulation, we designed multiple effective deep learning models achieving high beam management accuracy and spectrum efficiency improvement for massive MIMO and IRS-assisted systems. More general distributed deep learning model design considering CF access is under process.
For hardware demo, we purchased RoF devices and improved demo system scale. We simulated the IRS hardware design with simulation. We also made some basic IRS hardware models and measured the reflection performance.
Overall, the research is progressing smoothly.

Strategy for Future Research Activity

In the next step, we will continue with the following 3 aspects:
1. We will continue with the RoF nonlinear character analysis. Specifically, cooperational effect of the multiple RoF links.
2. Based on the study on modeling the CF-IRS network, we will publish journal and conference papers. Specifically, we will discuss how to reduce the training overhead and de-centralized cooperation among network devices.
3. We will accomplish international cooperation and domestic cooperation with the hardware implementation of IRS and design our apporach with deep learning algorithm loaded. We plan to apply for patents about the new IRS model with deep learning algorithm design.

Report

(1 results)
  • 2023 Research-status Report
  • Research Products

    (10 results)

All 2024 2023 Other

All Int'l Joint Research (1 results) Journal Article (2 results) (of which Int'l Joint Research: 2 results,  Peer Reviewed: 2 results,  Open Access: 1 results) Presentation (7 results) (of which Int'l Joint Research: 5 results,  Invited: 1 results)

  • [Int'l Joint Research] 北京郵電大学(中国)

    • Related Report
      2023 Research-status Report
  • [Journal Article] Robust Beamforming for Reconfigurable Intelligent Surface-Assisted Multi-cell Downlink Transmissions2024

    • Author(s)
      Gao Hui、Yang Xiaoyu、Chen Na、Chen Shuo、Yang Yue、Yuen Chau
    • Journal Title

      IEEE Transactions on Vehicular Technology

      Volume: 1 Issue: 5 Pages: 1-13

    • DOI

      10.1109/tvt.2023.3346906

    • Related Report
      2023 Research-status Report
    • Peer Reviewed / Int'l Joint Research
  • [Journal Article] Spatial attention and quantization-based contrastive learning framework for mmWave massive MIMO beam training2023

    • Author(s)
      Jia Haohui、Chen Na、Urakami Taisei、Gao Hui、Okada Minoru
    • Journal Title

      EURASIP Journal on Wireless Communications and Networking

      Volume: 2023 Issue: 1 Pages: 1-28

    • DOI

      10.1186/s13638-023-02277-w

    • Related Report
      2023 Research-status Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Presentation] Deep Learning Based Massive Array Signal Processing in Wireless Communication Systems2024

    • Author(s)
      Chen Na
    • Organizer
      the 2024 3rd International Workshop on Control, Communications and Multimedia (IWCCM 2024), co-host with the International Conference on Knowledge and Smart Technology (KST 2024)
    • Related Report
      2023 Research-status Report
    • Int'l Joint Research / Invited
  • [Presentation] Variable Multi-Band Metasurface Reflector with Controllable Direction Using Varactor Diodes Mounted Large-Via Mushroom-Type Structure2024

    • Author(s)
      Taisei Urakami, Tamami Maruyama, Akira Ono, Na Chen, and Minoru Okada
    • Organizer
      18th European Conference on Antennas and Propagation (EuCAP 2024)
    • Related Report
      2023 Research-status Report
    • Int'l Joint Research
  • [Presentation] バラクタダイオード装荷型マッシュルーム構造を用いた反射方向と周波数帯を考慮したメタサーフェス反射板2024

    • Author(s)
      浦上大世・丸山珠美・小野安季良・陳 娜・岡田 実
    • Organizer
      信学技報, vol. 123, no. 396, MW2023-190, pp. 78-83, 2024年2月.
    • Related Report
      2023 Research-status Report
  • [Presentation] IRS-Assisted mmWave Massive MIMO Systems Beam Training with Hybrid CNN Encoder-based Transformer Deep Learning Model2023

    • Author(s)
      Taisei Urakami, Haohui Jia, Na Chen, and Minoru Okada
    • Organizer
      2023 IEEE 98th Vehicular Technology Conference (VTC)
    • Related Report
      2023 Research-status Report
    • Int'l Joint Research
  • [Presentation] Individual-Driven Spiking-Mixer Deep Learning Model for IRS-Assisted mmWave Systems Beam Training2023

    • Author(s)
      Taisei Urakami, Haohui Jia, Dafang Zhao, Na Chen, Minoru Okada
    • Organizer
      2023 IEEE 11th Global Conference on Consumer Electronics (GCCE)
    • Related Report
      2023 Research-status Report
    • Int'l Joint Research
  • [Presentation] Integrated Beamforming and Resource Allocation in RIS-Assisted mmWave Networks based on Deep Reinforcement Learning2023

    • Author(s)
      Di Chen, Hui Gao, Na Chen and Ruohan Cao
    • Organizer
      2023 21st IEEE Interregional NEWCAS Conference (NEWCAS)
    • Related Report
      2023 Research-status Report
    • Int'l Joint Research
  • [Presentation] Memory-Driven MLP-Mixerを用いたIntelligent Reflecting Surface支援ミリ波システムにおけるビーム予測に関する研究2023

    • Author(s)
      浦上大世・賈 昊暉・陳 娜・岡田 実
    • Organizer
      信学技報, vol. 122, no. 411, MW2022-159, pp. 13-18, 2023年3月.
    • Related Report
      2023 Research-status Report

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Published: 2023-04-13   Modified: 2024-12-25  

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