Lightweight synchronization to NB-IoT enabled LEO Satellites through Doppler prediction

被引:1
|
作者
Zhou, Zheng [1 ]
Accettura, Nicola [1 ]
Prevost, Raoul [1 ]
Berthou, Pascal [1 ]
机构
[1] Univ Toulouse, CNRS, LAAS CNRS, UPS, Toulouse, France
关键词
NB-IoT; LEO Satellites; Doppler effect;
D O I
10.1109/WiMob58348.2023.10187879
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In the last decade, it has been quickly recognized that backhauling Low Power Wide Area Networks (LPWAN) through Low Earth Orbit (LEO) satellites paves the way to the development of novel applications for a truly ubiquitous Internet of Things (IoT). Among LPWAN communications technologies, Narrowband IoT (NB-IoT) does not suffer from interference by other concurrent technologies since it works on a licensed frequency spectrum. At the same time, thanks to its medium access scheme based on contention resolution and resource allocation, NB-IoT is a key enabler for the specific market slice of IoT applications requiring a good level of reliability. In the architectural configuration analyzed throughout this contribution, an NB-IoT low power User Equipment (UE) can communicate with a LEO satellite equipped with an Evolved Node B (eNB) for a time limited to the visibility window of that satellite from the UE position on the Earth. However, the Doppler effect inherent to the time-varying relative speed of the eNB needs to be dealt with additional resources. The solutions proposed until now are non-trivial, thus making the use of NBIoT for ground-to-satellite communications still expensive and energetically inefficient. Timely, this contribution proposes a procedure for a UE to infer the future values of the Doppler shift from the beacon signals so that frequency pre-compensation can be easily applied in the following interactions during the visibility time. The presented simulation results show that a UE needs to listen to about 10 beacon signals in 1 second to accurately and robustly predict the Doppler curve, thus enabling a lightweight (and eventually truly energy-efficient) implementation of NB-IoT over ground-to-satellite links.
引用
收藏
页码:218 / 223
页数:6
相关论文
共 43 条
  • [31] SDN-enabled Congestion Control Coordination and Coverage Class Adaptation in 5G NB-IoT Networks
    Lin, Shangjing
    Yu, Jianguo
    Chen, Yuanxiang
    Tian, Jin
    Ma, Ji
    2020 IEEE/CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA (ICCC WORKSHOPS), 2020, : 219 - 224
  • [32] Uncertainty-aware scheduling for effective data collection from environmental IoT devices through LEO satellites
    Xu, Haoran
    Chen, Xiaodao
    Huang, Xiaohui
    Min, Geyong
    Chen, Yunliang
    FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2025, 166
  • [33] Fairness-Aware Scheduling Optimization for NB-IoT in LEO Satellite Networks Using a 3D Spherical Coordinate System
    Lee, Byeongheon
    Lee, Ju-Hyung
    Ko, Young-Chai
    MILCOM 2023 - 2023 IEEE MILITARY COMMUNICATIONS CONFERENCE, 2023,
  • [34] Fast design of multiband fractal antennas through a system-by-design approach for NB-IoT applications
    Marco Salucci
    Nicola Anselmi
    Sotirios Goudos
    Andrea Massa
    EURASIP Journal on Wireless Communications and Networking, 2019
  • [35] Fast design of multiband fractal antennas through a system-by-design approach for NB-IoT applications
    Salucci, Marco
    Anselmi, Nicola
    Goudos, Sotirios
    Massa, Andrea
    EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING, 2019, 2019 (1)
  • [36] Implementation and Optimization of Narrow-Band Internet of Things (NB-IoT) Nodes Coverage Using Doppler Effect Shift Chips
    Elmazi, Donald
    Mehmeti, Fatjon
    Kulla, Elis
    Lecture Notes on Data Engineering and Communications Technologies, 2024, 189 : 150 - 162
  • [37] Maximizing Downlink User Connection Density in NOMA-aided NB-IoT Networks Through a Graph Matching Approach
    Mishra, Shashwat
    Salaun, Lou
    Gorce, Jean-Marie
    Chen, Chung Shue
    2022 IEEE 96TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2022-FALL), 2022,
  • [38] Lightweight Deep Learning-Based Model for Traffic Prediction in Fog-Enabled Dense Deployed IoT Networks
    Ateya, Abdelhamied A.
    Soliman, Naglaa F.
    Alkanhel, Reem
    Alhussan, Amel A.
    Muthanna, Ammar
    Koucheryavy, Andrey
    JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY, 2023, 18 (03) : 2275 - 2285
  • [39] Lightweight Deep Learning-Based Model for Traffic Prediction in Fog-Enabled Dense Deployed IoT Networks
    Abdelhamied A. Ateya
    Naglaa F. Soliman
    Reem Alkanhel
    Amel A. Alhussan
    Ammar Muthanna
    Andrey Koucheryavy
    Journal of Electrical Engineering & Technology, 2023, 18 : 2275 - 2285
  • [40] Reinforcement learning-enabled Intelligent Device-to-Device (I-D2D) communication in Narrowband Internet of Things (NB-IoT)
    Nauman, Ali
    Jamshed, Muhammad Ali
    Ali, Rashid
    Cengiz, Korhan
    Zulqarnain
    Kim, Sung Won
    COMPUTER COMMUNICATIONS, 2021, 176 : 13 - 22