Advanced Search
Volume 46 Issue 6
Jun.  2024
Turn off MathJax
Article Contents
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

Flexible Multiple Access Technology for Satellite Internet of Things

doi: 10.11999/JEIT231388
Funds:  The Natural Science Foundation of Chongqing (CSTB2023NSCQ-LZX0118), Beijing University of Posts and Telecommunications Excellent Ph.D. Students Foundation (CX2023139)
  • Received Date: 2023-12-18
  • Rev Recd Date: 2024-04-15
  • Available Online: 2024-04-27
  • Publish Date: 2024-06-30
  • Access latency and complexity in the satellite Internet of Things (IoT) are significantly reduced by the grant-free uplink random access based on Slotted ALOHA (S-ALOHA). However, with the increase of the number of IoT users, the collision probability of S-ALOHA is markedly increased, thereby impacting the performance of the system. This paper addresses the scenario of massive device uplink access in satellite IoT, focusing on the investigation of power resource control for IoT terminals to achieve maximization of system rate. A flexible multiple access scheme based on S-ALOHA is proposed. In the presence of collisions in the system, transmission is carried out using non-orthogonal multiple access technology, which mitigates the issue of repeated transmission of user information and reduces transmission latency. The sequential decision problem of maximizing system rate under the constraint of terminal power is modeled as a Markov process, and the Advantage Actor-Critic (A2C) method is employed to solve it. The simulation results indicate that the success rate of terminal access in scenarios with a massive number of IoT terminals is effectively ensured by the proposed flexible multiple access technology. Additionally, the resource allocation algorithm based on A2C is shown to outperform traditional resource allocation algorithms.
  • loading
  • [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.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(7)  / Tables(2)

    Article Metrics

    Article views (320) PDF downloads(38) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return