高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于深度强化学习的RIS辅助通感融合网络:挑战与机遇

陈真 杜晓宇 唐杰 WONGKat-Kit

陈真, 杜晓宇, 唐杰, WONGKat-Kit. 基于深度强化学习的RIS辅助通感融合网络:挑战与机遇[J]. 电子与信息学报, 2024, 46(9): 3467-3473. doi: 10.11999/JEIT240086
引用本文: 陈真, 杜晓宇, 唐杰, WONGKat-Kit. 基于深度强化学习的RIS辅助通感融合网络:挑战与机遇[J]. 电子与信息学报, 2024, 46(9): 3467-3473. doi: 10.11999/JEIT240086
CHEN Zhen, DU Xiaoyu, TANG Jie, WONG Kat-Kit. DRL-based RIS-assisted ISAC Network: Challenges and Opportunities[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3467-3473. doi: 10.11999/JEIT240086
Citation: CHEN Zhen, DU Xiaoyu, TANG Jie, WONG Kat-Kit. DRL-based RIS-assisted ISAC Network: Challenges and Opportunities[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3467-3473. doi: 10.11999/JEIT240086

基于深度强化学习的RIS辅助通感融合网络:挑战与机遇

doi: 10.11999/JEIT240086
基金项目: 国家自然科学基金(62371197),广东省自然科学基金(2022A1515011189),东南大学开放课题(K202411)
详细信息
    作者简介:

    陈真:男,副研究员,研究方向为智能反射面通信、信道估计及波束管理

    杜晓宇:女,硕士生,研究方向为智能反射面信道估计

    唐杰:男,教授,研究方向为无线携能通信、智能反射面、绿色通信等

    WONGKat-Kit:男,教授,研究方向为5G, 6G和流体天线系统等

    通讯作者:

    陈真 chenz.scut@gmail.com

  • 中图分类号: TN929.5

DRL-based RIS-assisted ISAC Network: Challenges and Opportunities

Funds: The National Natural Science Foundation of China (62371197), The National Natural Science Foundation of Guangdong (2022A1515011189), The Open Project of Southeast University (K202411)
  • 摘要: 随着深度强化学习(DRL)技术的广泛应用,基于DRL的可重构智能表面(RIS)辅助的通信感知一体化(ISAC)展现出巨大的潜力。然而,由于数据卸载和模型训练的高成本,基于现有ISAC框架实现网络智能仍面临着巨大的挑战。为了克服该问题,该文深入分析了DRL技术在ISAC领域的应用,探讨了RIS辅助的ISAC建模及其解决方案,该技术能够解决覆盖区域受限、算法复杂度高以及高频传输等问题。为了推动这些技术的实施,该文进一步讨论了RIS辅助ISAC网络中DRL技术的未来发展趋势,包括潜在的应用和需要解决的问题。
  • 图  1  RIS辅助ISAC系统的应用演示场景

    图  2  RIS辅助ISAC系统的DDPG设计

    图  3  RIS辅助ISAC系统的算力网络

    图  4  损失函数值与迭代次数的关系

    图  5  不同ISAC方案雷达探测率与迭代次数的关系

    表  1  基于深度学习的RIS辅助通信的最新进展

    优化指标 RIS指标 DRL指标 场景 技术 结果
    最大化保密率[9] 相移控制
    波束赋形设计
    参数设计 BS到RIS
    多用户
    DQN
    MDP
    提高保密率
    最大化加权和速率[11] 相移控制
    波束赋形设计
    参数设计 BS到RIS
    多用户
    比较检索法
    DQNN
    提高加权和速率
    提高频谱效率[14] 相移控制 互信息优化 BS到用户 DDPG
    TD3
    提高频谱效率
    最大化和速率[15] 相移控制
    波束赋形设计
    环境学习 RIS到用户
    多用户
    NOMA协议
    DRSAC
    提高和速率
    最大化总保密率[16] 相移控制
    波束赋形设计
    环境学习
    参数设计
    多用户
    多窃听者
    DDPG
    MDP
    提高总保密率
    下载: 导出CSV

    表  2  参数设置

    参数名称参数值
    RIS个数10
    训练大小1000
    训练学习率0.001
    迭代次数20
    测试集样本个数500
    下载: 导出CSV
  • [1] LIU An, HUANG Zhe, LI Min, et al. A survey on fundamental limits of integrated sensing and communication[J]. IEEE Communications Surveys & Tutorials, 2022, 24(2): 994–1034. doi: 10.1109/COMST.2022.3149272.
    [2] CHEN Zhen, TANG Jie, HUANG Lei, et al. Robust target positioning for reconfigurable intelligent surface assisted MIMO radar systems[J]. IEEE Transactions on Vehicular Technology, 2023, 72(11): 15098–15102. doi: 10.1109/TVT.2023.3284454.
    [3] MEALEY R M. A method for calculating error probabilities in a radar communication system[J]. IEEE Transactions on Space Electronics and Telemetry, 1963, 9(2): 37–42. doi: 10.1109/TSET.1963.4337601.
    [4] ZHANG J A, RAHMAN M L, WU Kai, et al. Enabling joint communication and radar sensing in mobile networks—a survey[J]. IEEE Communications Surveys & Tutorials, 2022, 24(1): 306–345. doi: 10.1109/COMST.2021.3122519.
    [5] TONEX. Introduction to 6G | IMT-2030[EB/OL]. https://www.tonex.com/training-courses/introduction-to-6g-imt-2030/, 2020.
    [6] CHEN Zhen, HUANG Lei, XIA Shuqiang, et al. Parallel channel estimation for RIS-assisted internet of things[J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(8): 9762–9773. doi: 10.1109/TITS.2024.3364248.
    [7] Communication Network., ZTE and China Unicom Achieve World's First 5G Mid-Band Network Verification of Reconfigurable Intelligent Surface in External Networks[EB/OL] https://www.c114.com.cn/news/127/a1167167.html, 2021.

    Communication Network.,ZTE and China Unicom Achieve World's First 5G Mid-Band Network Verification of Reconfigurable Intelligent Surface in External Networks[EB/OL] https://www.c114.com.cn/news/127/a1167167.html, 2021.
    [8] CHEN Zhen, TANG Jie, ZHANG Xiuyin, et al. Hybrid evolutionary-based sparse channel estimation for IRS-assisted mmWave MIMO systems[J]. IEEE Transactions on Wireless Communications, 2022, 21(3): 1586–1601. doi: 10.1109/TWC.2021.3105405.
    [9] HUANG Chongwen, MO Ronghong, and YUEN C. Reconfigurable intelligent surface assisted multiuser MISO systems exploiting deep reinforcement learning[J]. IEEE Journal on Selected Areas in Communications, 2020, 38(8): 1839–1850. doi: 10.1109/JSAC.2020.3000835.
    [10] XU Wangyang, AN Jiancheng, XU Yongjun, et al. Time-varying channel prediction for RIS-assisted MU-MISO networks via deep learning[J]. IEEE Transactions on Cognitive Communications and Networking, 2022, 8(4): 1802–1815. doi: 10.1109/TCCN.2022.3188153.
    [11] YANG Helin, XIONG Zehui, ZHAO Jun, et al. Deep reinforcement learning-based intelligent reflecting surface for secure wireless communications[J]. IEEE Transactions on Wireless Communications, 2021, 20(1): 375–388. doi: 10.1109/TWC.2020.3024860.
    [12] XU Wangyang, GAN Lu, and HUANG Chongwen. A robust deep learning-based beamforming design for RIS-assisted multiuser MISO communications with practical constraints[J]. IEEE transactions on Cognitive Communications and Networking, 2022, 8(2): 694–706. doi: 10.1109/TCCN.2021.3128605.
    [13] DEMIR Ö T and BJÖRNSON E. Is channel estimation necessary to select phase-shifts for RIS-assisted massive MIMO?[J]. IEEE Transactions on Wireless Communications, 2022, 21(11): 9537–9552. doi: 10.1109/TWC.2022.3177700.
    [14] SAIKIA P, SINGH K, TAGHIZADEH O, et al. DRL algorithms for efficient spectrum sharing in RIS-aided MIMO radar and cellular systems[C]. MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM), Rockville, USA, 2022: 55–60. doi: 10.1109/MILCOM55135.2022.10017985.
    [15] ZHAO Jingjing, YU Lanchenhui, CAI Kaiquan, et al. RIS-aided ground-aerial NOMA communications: A distributionally robust DRL approach[J]. IEEE Journal on Selected Areas in Communications, 2022, 40(4): 1287–1301. doi: 10.1109/JSAC.2022.3143230.
    [16] PENG Zhangjie, ZHANG Zhibo, KONG Lei, et al. Deep reinforcement learning for RIS-aided multiuser full-duplex secure communications with hardware impairments[J]. IEEE Internet of Things Journal, 2022, 9(21): 21121–21135. doi: 10.1109/JIOT.2022.3177705.
    [17] 张在琛, 江浩. 智能超表面使能无人机高能效通信信道建模与传输机理分析[J]. 电子学报, 2023, 51(10): 2623–2634. doi: 10.12263/DZXB.20221352.

    ZHANG Zaichen and JIANG Hao. Channel modeling and characteristics analysis for high energy-efficient RIS-assisted UAV communications[J]. Acta Electronica Sinica, 2023, 51(10): 2623–2634. doi: 10.12263/DZXB.20221352.
    [18] ZHOU Hao, EROL-KANTARCI M, LIU Yuanwei, et al. Heuristic algorithms for RIS-assisted wireless networks: Exploring heuristic-aided machine learning[J]. IEEE Wireless Communications, 2024, 31(4): 106–114. doi: 10.1109/MWC.010.2300321.
  • 加载中
图(5) / 表(2)
计量
  • 文章访问数:  142
  • HTML全文浏览量:  27
  • PDF下载量:  14
  • 被引次数: 0
出版历程
  • 收稿日期:  2024-02-22
  • 修回日期:  2024-08-13
  • 网络出版日期:  2024-08-27
  • 刊出日期:  2024-09-26

目录

    /

    返回文章
    返回