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一种基于预留-重用联合的C-V2X通信Q学习型半持续调度算法

王萍 陆岩 王帅 姚汪鼎

王萍, 陆岩, 王帅, 姚汪鼎. 一种基于预留-重用联合的C-V2X通信Q学习型半持续调度算法[J]. 电子与信息学报, 2022, 44(8): 2785-2791. doi: 10.11999/JEIT210543
引用本文: 王萍, 陆岩, 王帅, 姚汪鼎. 一种基于预留-重用联合的C-V2X通信Q学习型半持续调度算法[J]. 电子与信息学报, 2022, 44(8): 2785-2791. doi: 10.11999/JEIT210543
WANG Ping, LU Yan, WANG Shuai, YAO Wangding. A Reservation and Reuse Combined Q-learning Semi Persistent Scheduling for C-V2X Communication[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2785-2791. doi: 10.11999/JEIT210543
Citation: WANG Ping, LU Yan, WANG Shuai, YAO Wangding. A Reservation and Reuse Combined Q-learning Semi Persistent Scheduling for C-V2X Communication[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2785-2791. doi: 10.11999/JEIT210543

一种基于预留-重用联合的C-V2X通信Q学习型半持续调度算法

doi: 10.11999/JEIT210543
详细信息
    作者简介:

    王萍:女,1973年生,教授,博士生导师,研究方向为5G无线网络关键技术、V2X通信、异构网络优化理论、移动物联网

    陆岩:女,1996年生,硕士生,研究方向为5G移动通信、V2X通信、无线资源管理

    王帅:男,1994年生,博士生,研究方向为V2X通信、空间调制、车载网络与5G移动通信

    姚汪鼎:男,1996年生,硕士生,研究方向为V2X通信、机会网络、机会路由与5G移动通信

    通讯作者:

    王萍 pingwang@dhu.edu.cn

  • 中图分类号: TN915.04

A Reservation and Reuse Combined Q-learning Semi Persistent Scheduling for C-V2X Communication

  • 摘要: 第3代合作伙伴计划(3GPP)模式4提供了一种直连通信的接入方式,以支持车联网C-V2X应用。然而V2X网络动态变化的业务负载和车辆移动性导致信道质量不稳定,加剧了传输过程中分组碰撞问题。为了满足高可靠低时延的V2X通信需求,该文针对动态负载环境下的高效分布式资源分配策略,提出一种预留-重用联合的Q学习型半持续调度(RRC-QSPS)算法。该文首先对模式4中现有的半持续调度(SPS)算法分组碰撞问题进行理论建模,分析了影响碰撞概率的关键参数,继而提出了动态业务环境下车辆智能体的强化Q学习模型,建立包括预留-重用联合的动作空间与Q目标函数,并通过$ \varepsilon $-贪心算法求解,智能决策动态负载环境下无线资源的预留与重用。仿真对比结果表明,相比于已有的Lookahead-SPS优化算法,RRC-QSPS算法在高速高负载场景下分组接收率提高了7%,数据包更新时延降低了10%。
  • 图  1  标准SPS算法的资源预留过程

    图  2  强化学习原理图

    图  3  RRC-QSPS算法结构图

    图  4  碰撞概率与发包率的关系

    图  5  分组接收率与发包率的关系

    图  6  数据包更新时延与发包率的关系

    图  7  平均吞吐量与发包率的关系

    表  1  5G网络技术及优势

    5G网络技术优势
    软件定义网络技术提升数据网络灵活性
    网络功能虚拟化技术提升网络运维效率
    网络切片技术满足不同应用场景需求
    大规模MIMO技术提升网络的通信容量
    设备到设备通信技术支持直连通信,降低端到端时延
    下载: 导出CSV

    表  2  算法1 RRC-QSPS

     输入:C1,C2,NCAMRs, Q(st, at)
     输出:CAMRid
     (1) 初始化Q学习参数和SPS算法参数RC←random(C1,C2),
       CAMRid←random(1, NCAMRs)
     (2) 观察当前状态st
     (3) LOOP
     (4) IF RC /= 0 THEN
     (5)  RC←RC–1
     (6) ELSE
     (7)   由式(18)选择动作at并执行,即更新RC和RP值,保持或
         重选CAMRid
     (8) ELSE IF
     (9) 观察后续状态st+1,并由式(16)计算回报函数Rt
     (10) 根据式(17)更新Q(st, at)值
     (11) END LOOP
    下载: 导出CSV

    表  3  仿真参数和配置

    参数参数值
    仿真时间50 s
    仿真区域中的车辆数(ρ)100
    平均车速120 km/h(方差=3)
    道路长度1000 m
    车道数2(每个方向1个)
    CAM大小190 Bytes
    发包率5~50 pps
    感测距离(raw)150 m
    发射功率15 dBm
    传播模型WINNER+, B1
    阴影衰落方差3 dB
    路径损耗指数(β)2.75
    调制和编码方案MCS 7 (QPSK)
    下载: 导出CSV
  • [1] JEON Y and KIM H. An explicit reservation-augmented resource allocation scheme for C-V2X Sidelink mode 4[J]. IEEE Access, 2020, 8: 147241–147255. doi: 10.1109/ACCESS.2020.3015549
    [2] BAZZI A, CECCHINI G, ZANELLA A, et al. Study of the impact of PHY and MAC parameters in 3GPP C-V2V mode 4[J]. IEEE Access, 2018, 6: 71685–71698. doi: 10.1109/ACCESS.2018.2883401
    [3] NAIK G, CHOUDHURY B, and PARK J M. IEEE 802.11 bd & 5G NR V2X: Evolution of radio access technologies for V2X communications[J]. IEEE Access, 2019, 7: 70169–70184. doi: 10.1109/ACCESS.2019.2919489
    [4] WANG Shuai, LIU Yan, ZHU Jie, et al. A novel collision supervision and avoidance algorithm for scalable MAC of vehicular networks[J]. Chinese Journal of Electronics, 2021, 30(1): 164–170. doi: 10.1049/cje.2020.12.001
    [5] MOLINA-MASEGOSA R and GOZALVEZ J. LTE-V for sidelink 5G V2X vehicular communications: A new 5G technology for short-range vehicle-to-everything communications[J]. IEEE Vehicular Technology Magazine, 2017, 12(4): 30–39. doi: 10.1109/MVT.2017.2752798
    [6] GONZALEZ-MARTÍN M, SEPULCRE M, MOLINA-MASEGOSA R, et al. Analytical models of the performance of C-V2X mode 4 vehicular communications[J]. IEEE Transactions on Vehicular Technology, 2019, 68(2): 1155–1166. doi: 10.1109/TVT.2018.2888704
    [7] JUNG S Y, CHEON H R, and KIM J H. Reducing consecutive collisions in sensing based semi persistent scheduling for cellular-V2X[C]. 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), Honolulu, USA, 2019: 1–5.
    [8] JEON Y, KUK S, and KIM H. Reducing message collisions in sensing-based semi-persistent scheduling (SPS) by using reselection lookaheads in cellular V2X[J]. Sensors, 2018, 18(12): 4388. doi: 10.3390/s18124388
    [9] BONJORN N, FOUKALAS F, and POP P. Enhanced 5G V2X services using sidelink device-to-device communications[C]. 2018 17th Annual Mediterranean ad HOC Networking Workshop (Med-Hoc-Net), Capri, Italy, 2018: 1–7.
    [10] HONNAIAH P J, MATURO N, and CHATZINOTAS S. Foreseeing semi-persistent scheduling in mode-4 for 5G enhanced V2X communication[C]. 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, USA, 2020: 1–2.
    [11] 余翔, 陈晓东, 王政, 等. 基于LTE-V2X的车联网资源分配算法[J]. 计算机工程, 2021, 47(2): 188–193.

    YU Xiang, CHEN Xiaodong, WANG Zheng, et al. Resource allocation algorithm for internet of vehicles based on LTE-V2X[J]. Computer Engineering, 2021, 47(2): 188–193.
    [12] 金久一, 邱恭安. C-V2X通信中资源分配与功率控制联合优化[J/OL]. 计算机工程, 2020: 1–10. https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CAPJ&dbname=CAPJLAST&filename=JSJC20201014004&v=fpDLQvPDFGcn3qeXmuRIceO0Zyyaau3ClfB8VIRe3GnypH%25mmd2FWjA8xWhfkjlUBdOBz, 2020.

    JIN Jiuyi and QIU Gongan. Joint optimization of resource allocation and power control in C-V2X communications[J/OL]. Computer Engineering, 2020: 1–10. https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CAPJ&dbname=CAPJLAST&filename=JSJC20201014004&v=fpDLQvPDFGcn3qeXmuRIceO0Zyyaau3ClfB8VIRe3GnypH%25mmd2FWjA8xWhfkjlUBdOBz, 2020.
    [13] CAMPOLO C, MOLINARO A, ROMEO F, et al. 5G NR V2X: On the impact of a flexible numerology on the autonomous sidelink mode[C]. 2019 IEEE 2nd 5G World Forum (5GWF), Dresden, Germany, 2019: 102–107.
    [14] BONJORN N, FOUKALAS F, CAÑELLAS F, et al. Cooperative resource allocation and scheduling for 5G eV2X services[J]. IEEE Access, 2019, 7: 58212–58220. doi: 10.1109/ACCESS.2018.2889190
    [15] HAIDER A and HWANG S H. Adaptive transmit power control algorithm for sensing-based semi-persistent scheduling in C-V2X mode 4 communication[J]. Electronics, 2019, 8(8): 846. doi: 10.3390/electronics8080846
    [16] YE Hao, LI G Y, and JUANG B H F. Deep reinforcement learning based resource allocation for V2V communications[J]. IEEE Transactions on Vehicular Technology, 2019, 68(4): 3163–3173. doi: 10.1109/TVT.2019.2897134
    [17] 陈前斌, 管令进, 李子煜, 等. 基于深度强化学习的异构云无线接入网自适应无线资源分配算法[J]. 电子与信息学报, 2020, 42(6): 1468–1477. doi: 10.11999/JEIT190511

    CHEN Qianbin, GUAN Lingjin, LI Ziyu, et al. Deep reinforcement learning-based adaptive wireless resource allocation algorithm for heterogeneous cloud wireless access network[J]. Journal of Electronics &Information Technology, 2020, 42(6): 1468–1477. doi: 10.11999/JEIT190511
    [18] SUTTON R S and BARTO A G. Reinforcement learning: An introduction[J]. IEEE Transactions on Neural Networks, 1998, 9(5): 1054.
    [19] CECCHINI G, BAZZI A, MASINI B M, et al. LTEV2Vsim: An LTE-V2V simulator for the investigation of resource allocation for cooperative awareness[C]. 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Naples, Italy, 2017: 80–85.
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出版历程
  • 收稿日期:  2021-06-08
  • 修回日期:  2021-08-25
  • 网络出版日期:  2021-09-29
  • 刊出日期:  2022-08-17

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