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面向卫星物联网的柔性多址接入技术

庞明亮 王朝炜 吴彤 陈佳彬 黄赛 江帆 张君毅

庞明亮, 王朝炜, 吴彤, 陈佳彬, 黄赛, 江帆, 张君毅. 面向卫星物联网的柔性多址接入技术[J]. 电子与信息学报, 2024, 46(6): 2497-2505. doi: 10.11999/JEIT231388
引用本文: 庞明亮, 王朝炜, 吴彤, 陈佳彬, 黄赛, 江帆, 张君毅. 面向卫星物联网的柔性多址接入技术[J]. 电子与信息学报, 2024, 46(6): 2497-2505. doi: 10.11999/JEIT231388
PANG Mingliang, WANG Chaowei, WU Tong, CHEN Jiabin, HUANG Sai, JIANG Fan, ZHANG Junyi. Flexible Multiple Access Technology for Satellite Internet of Things[J]. Journal of Electronics & Information Technology, 2024, 46(6): 2497-2505. doi: 10.11999/JEIT231388
Citation: PANG Mingliang, WANG Chaowei, WU Tong, CHEN Jiabin, HUANG Sai, JIANG Fan, ZHANG Junyi. Flexible Multiple Access Technology for Satellite Internet of Things[J]. Journal of Electronics & Information Technology, 2024, 46(6): 2497-2505. doi: 10.11999/JEIT231388

面向卫星物联网的柔性多址接入技术

doi: 10.11999/JEIT231388
基金项目: 重庆市自然科学基金创新发展联合基金(CSTB2023NSCQ-LZX0118),北京邮电大学博士生创新基金(CX2023139)
详细信息
    作者简介:

    庞明亮:男,博士生,研究方向为卫星通信、多址接入技术和资源管理等

    王朝炜:男,博士,副教授,研究方向为下一代移动通信技术、无线传感器与物联网技术等

    吴彤:男,博士,高工,研究方向为卫星互联网应用技术、通导一体化、高精度定位等

    陈佳彬:男,硕士生,研究方向为卫星通信、多址接入技术等

    黄赛:男,博士,副教授,研究方向为基于机器学习的智能信号处理、通用无线信号快速检测与深度识别等

    江帆:女,博士,教授,研究方向为基于人工智能的边缘计算及缓存技术、D2D通信技术、5G超密集异构网络中的无线资源管理等

    张君毅:男,博士,副教授,研究方向为物理电子学和可见光通信等

    通讯作者:

    王朝炜 wangchaowei@bupt.edu.cn

  • 中图分类号: TN927

Flexible Multiple Access Technology for Satellite Internet of Things

Funds: The Natural Science Foundation of Chongqing (CSTB2023NSCQ-LZX0118), Beijing University of Posts and Telecommunications Excellent Ph.D. Students Foundation (CX2023139)
  • 摘要: 基于时隙ALOHA(S-ALOHA)的免授权上行随机接入能够显著降低卫星物联网(IoT)中的接入时延和复杂度。然而,随着物联网用户数量的增加,S-ALOHA碰撞概率会显著增加,从而影响系统性能。该文针对卫星物联网中存在海量设备上行接入的场景,专注于研究物联网终端的功率资源控制,以实现最大化系统和速率的目标。具体而言,该文提出基于S-ALOHA的柔性多址接入。当系统中存在碰撞时,采用非正交多址技术进行传输,从而避免了用户信息反复重传的问题,降低了传输时延。为了在终端功率受限的情况下实现系统和速率的最大化,该文将序列决策问题建模为马尔可夫决策过程,并采用优势演员-评论家算法(A2C)进行求解。仿真结果表明,所提出的柔性多址接入技术能够在海量物联网终端的场景下有效保证终端的接入成功率。同时,基于A2C的资源分配算法相较于传统的资源分配算法表现更为优越。
  • 图  1  卫星物联网柔性上行接入场景

    图  2  S-ALOHA与柔性多址接入对比

    图  3  基于A2C的资源分配框架

    图  4  接入成功率对比

    图  5  基于A2C的资源分配算法收敛性

    图  6  不同算法复杂度对比

    图  7  不同用户数时系统速率对比

    1  基于A2C的功率分配算法

     输入:波束内活跃用户数${M_{\mathrm{a}}}$,波束数$K$,服务时隙数$N$。
     输出:功率分配系数矩阵$ {\boldsymbol{\alpha}} = [{{\boldsymbol{\alpha }}_{_1}},{{\boldsymbol{\alpha}} _{_2}}, \cdots ,{{\boldsymbol{\alpha}} _{_K}}] $
     (1)初始化神经网络参数${{\boldsymbol{\theta }}_{\mathrm{a}}}$和${{\boldsymbol{\theta}} _{\mathrm{c}}}$
     (2) For $n = 1:{\text{Episode}}$ do
     (3)  初始化环境
     (4)  For $t = 1:N$ do
     (5)   获取当前状态${{\boldsymbol{s}}_t} \in S$
     (6)   if ${{\boldsymbol{s}}_t}$不是最终状态 do
     (7)    根据策略${\pi _\theta }$选择动作${{\boldsymbol{a}}_t}$
     (8)    将状态更新为${{\boldsymbol{s}}_{t + 1}}$并获得奖励${r_t}$
     (9)   End if
     (10) 计算$L\left( {{{\boldsymbol{\theta}} _{\mathrm{a}}}} \right)$和TD-error
     (11) 更新参数${{\boldsymbol{\theta}} _{\mathrm{a}}}$
     (12) 更新参数${{\boldsymbol{\theta }}_{\mathrm{c}}}$
     (13) End for
     (14) End for
    下载: 导出CSV

    表  1  仿真参数设置

    参数 含义 取值
    $ {h_0} $ 卫星轨道高度 600 km
    ${f_{\mathrm{c}}}$ 工作频段 Ka
    $K$ 系统波束数 7
    ${M_{\mathrm{a}}}$ 波束内活跃用户数 3
    $M$ 单个波束内总用户数 30
    $\eta $ 活跃用户占比 10%
    $N$ 服务时隙数 3
    ${G_{\mathrm{t}}}$ 簇头用户最大发射天线增益 43.2 dBi
    ${G_{\mathrm{r}}}$ 卫星最大接收天线增益 38.5 dBi
    ${P_k}$ 簇头用户最大发射功率 2 W
    ${N_0}$ 高斯白噪声功率谱密度 –174 dBm/ Hz
    下载: 导出CSV
  • [1] BAI Tingqian, HUANG C Y, and LEE Y. Reliably route IoT packets in software defined mmWave mesh networks[J]. IEEE Networking Letters, 2023, 5(1): 50–54. doi: 10.1109/LNET.2023.3239120.
    [2] SHI Liqin, YE Yinghui, CHU Xiaoli, et al. Computation bits maximization in a backscatter assisted wirelessly powered MEC network[J]. IEEE Communications Letters, 2021, 25(2): 528–532. doi: 10.1109/LCOMM.2020.3027294.
    [3] YU Peng, LI Yijing, ZHANG Manjun, et al. Self-organized and distributed green resource allocation for space-air-ground IoT networks[J]. IEEE Internet of Things Journal, 2023, 10(11): 9385–9397. doi: 10.1109/JIOT.2022.3222238.
    [4] 李凯, 李峰, 杨伟铭. 天基物联网: 基本概念、体系架构及发展趋势[J]. 电讯技术, 2023, 63(2): 281–290. doi: 10.20079/j.issn.1001-893x.211207003.

    LI Kai, LI Feng, and YANG Weiming. Space-based internet of things: Basic concepts, system architecture and development trends[J]. Telecommunication Engineering, 2023, 63(2): 281–290. doi: 10.20079/j.issn.1001-893x.211207003.
    [5] HUANG Yudian, LI Meng, YU F R, et al. Resources scheduling for ambient backscatter communication-based intelligent IIoT: A collective deep reinforcement learning method[J]. IEEE Transactions on Cognitive Communications and Networking, 2024, 10(2): 634–648. doi: 10.1109/TCCN.2023.3330065.
    [6] HUANG Yudian, LI Meng, YU F R, et al. Performance optimization for energy-efficient industrial internet of things based on ambient backscatter communication: An A3C-FL approach[J]. IEEE Transactions on Green Communications and Networking, 2023, 7(3): 1121–1134. doi: 10.1109/TGCN.2023.3260199.
    [7] SHI Liqin, YE Yinghui, CHU Xiaoli, et al. Energy-efficient resource allocation for backscatter-assisted wireless powered MEC[J]. IEEE Transactions on Vehicular Technology, 2023, 72(7): 9591–9596. doi: 10.1109/TVT.2023.3246237.
    [8] LI Meng, YU F R, SI Pengbo, et al. Intelligent resource optimization for blockchain-enabled IoT in 6G via collective reinforcement learning[J]. IEEE Network, 2022, 36(6): 175–182. doi: 10.1109/MNET.105.2100516.
    [9] LIN Guozhi, GE Jingguo, and WU Yulei. Toward zero touch networks: From the perspective of hierarchical language systems[J]. IEEE Network, 2022, 36(6): 260–268. doi: 10.1109/MNET.001.2200037.
    [10] WANG Ji, ZHOU Longfei, YANG Kai, et al. Multicast precoding for multigateway multibeam satellite systems with feeder link interference[J]. IEEE Transactions on Wireless Communications, 2019, 18(3): 1637–1650. doi: 10.1109/TWC.2019.2894823.
    [11] YAN Lanting, DING Xiaojin, and ZHANG Gengxin. Dynamic channel allocation aided random access for SDN-enabled LEO satellite IoT[J]. Journal of Communications and Information Networks, 2021, 6(2): 134–141. doi: 10.23919/JCIN.2021.9475123.
    [12] AKHLAGHPASAND H and SHAH-MANSOURI V. Traffic offloading probability for integrated LEO satellite-terrestrial networks[J]. IEEE Communications Letters, 2023, 27(9): 2413–2416. doi: 10.1109/LCOMM.2023.3298572.
    [13] CUI Huanxi, ZHANG Jun, GENG Yuhui, et al. Space-air-ground integrated network (SAGIN) for 6G: Requirements, architecture and challenges[J]. China Communications, 2022, 19(2): 90–108. doi: 10.23919/JCC.2022.02.008.
    [14] CHAUHAN A, GHOSH S, and JAISWAL A. RIS partition-assisted non-orthogonal multiple access (NOMA) and quadrature-NOMA with imperfect SIC[J]. IEEE Transactions on Wireless Communications, 2023, 22(7): 4371–4386. doi: 10.1109/TWC.2022.3224645.
    [15] LIU Rui, GUO Kefeng, AN Kang, et al. Resource allocation for cognitive satellite-HAP-terrestrial networks with non-orthogonal multiple access[J]. IEEE Transactions on Vehicular Technology, 2023, 72(7): 9659–9663. doi: 10.1109/TVT.2023.3252642.
    [16] ZHANG Zheng, CHEN Jian, WU Qingqing, et al. Securing NOMA networks by exploiting intelligent reflecting surface[J]. IEEE Transactions on Communications, 2022, 70(2): 1096–1111. doi: 10.1109/TCOMM.2021.3126636.
    [17] SHI Liqin, YE Yinghui, CHU Xiaoli, et al. Computation energy efficiency maximization for a NOMA-based WPT-MEC network[J]. IEEE Internet of Things Journal, 2021, 8(13): 10731–10744. doi: 10.1109/JIOT.2020.3048937.
    [18] YANG Mengqi, CHEN Jian, DING Zhiguo, et al. Rate-aware user pair scheduling with joint power allocation and decoding order selection in NOMA systems[J]. IEEE Transactions on Communications, 2023, 71(9): 5303–5319. doi: 10.1109/TCOMM.2023.3292479.
    [19] SREYA G, SAIGADHA S, MANKAR P D, et al. Adaptive rate NOMA for cellular IoT networks[J]. IEEE Wireless Communications Letters, 2022, 11(3): 478–482. doi: 10.1109/LWC.2021.3132932.
    [20] HU Yingmeng, PENG Lin, and LIU Yan. Design and analysis of a dynamic access class barring NOMA random access algorithm[J]. IEEE Communications Letters, 2022, 26(12): 3054–3058. doi: 10.1109/LCOMM.2022.3204567.
    [21] ZHANG Ningbo and ZHU Xuzhen. A hybrid grant NOMA random access for massive MTC service[J]. IEEE Internet of Things Journal, 2023, 10(6): 5490–5505. doi: 10.1109/JIOT.2022.3222622.
    [22] GAO Yayu, FANG Shuangfeng, SONG Xiangchen, et al. When aloha and CSMA coexist: Modeling, fairness, and throughput optimization[J]. IEEE Transactions on Wireless Communications, 2022, 21(10): 8163–8178. doi: 10.1109/TWC.2022.3164463.
    [23] CHEN Ziru, ZHANG Ran, LIU Yong, et al. Performance study of cybertwin-assisted random access NOMA[J]. IEEE Internet of Things Journal, 2021, 8(22): 16279–16289. doi: 10.1109/JIOT.2021.3100457.
    [24] CAO Shengbin and HOU Fen. On the mathematical modeling and optimization for the energy efficiency performance of CSMA-NOMA random access networks with channel inversion[J]. IEEE Transactions on Wireless Communications, 2023, 22(4): 2867–2884. doi: 10.1109/TWC.2022.3215227.
    [25] RAMATRYANA I N A, SATRYA G B, and SHIN S Y. Adaptive traffic load in IRSA-NOMA prioritizing emergency devices for 6G enabled massive IoT[J]. IEEE Wireless Communications Letters, 2021, 10(12): 2713–2717. doi: 10.1109/LWC.2021.3113048.
    [26] YU Hanxiao, ZHAO Hanyu, FEI Zesong, et al. Deep-reinforcement-learning-based NOMA-aided slotted ALOHA for LEO satellite IoT networks[J]. IEEE Internet of Things Journal, 2023, 10(20): 17772–17784. doi: 10.1109/JIOT.2023.3277836.
    [27] TUBIANA D A, FARHAT J, BRANTE G, et al. Q-learning NOMA random access for IoT-satellite terrestrial relay networks[J]. IEEE Wireless Communications Letters, 2022, 11(8): 1619–1623. doi: 10.1109/LWC.2022.3169109.
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出版历程
  • 收稿日期:  2023-12-18
  • 修回日期:  2024-04-15
  • 网络出版日期:  2024-04-27
  • 刊出日期:  2024-06-30

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