Smart Grid and Internet of Things. 4th EAI International Conference, SGIoT 2020, TaiChung, Taiwan, December 5–6, 2020, Proceedings

Research Article

Dynamic Time Slot Adjustment Based Beamform Training for the Next Generation Millimeter Wave WLAN

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  • @INPROCEEDINGS{10.1007/978-3-030-69514-9_27,
        author={Zhaotun Feng and Yong Fang and Mao Yang and Zhongjiang Yan and Bo Li},
        title={Dynamic Time Slot Adjustment Based Beamform Training for the Next Generation Millimeter Wave WLAN},
        proceedings={Smart Grid and Internet of Things. 4th EAI International Conference, SGIoT 2020, TaiChung, Taiwan, December 5--6, 2020, Proceedings},
        proceedings_a={SGIOT},
        year={2021},
        month={7},
        keywords={Millimeter wave WLAN 802.11ad Beam training},
        doi={10.1007/978-3-030-69514-9_27}
    }
    
  • Zhaotun Feng
    Yong Fang
    Mao Yang
    Zhongjiang Yan
    Bo Li
    Year: 2021
    Dynamic Time Slot Adjustment Based Beamform Training for the Next Generation Millimeter Wave WLAN
    SGIOT
    Springer
    DOI: 10.1007/978-3-030-69514-9_27
Zhaotun Feng1, Yong Fang1, Mao Yang2, Zhongjiang Yan2, Bo Li2
  • 1: Chang’an University
  • 2: Northwestern Polytechnical University

Abstract

In recent years, people have put forward higher and higher requirements for high-speed communications within a local area. Millimeter-wave WLAN has attracted much attention from academia and industry by virtue of its ultra-large bandwidth and short-range coverage. Beam training is a key technology of millimeter wave WLAN. The quality of beam training is related to communication performance and even communication. However, as the number of nodes continues to increase, the beam training efficiency of the traditional millimeter wave WLAN is very low, which affects system performance. This paper proposes a beamforming training method based on dynamic time slot adjustment for the next generation millimeter wave WLAN. The beam training slot in the subsequent beacon interval (BI) can be adjusted according to the completion of the beam training in the previous BI, thereby improving the efficiency of beam training. The simulation results prove that the method proposed in this paper can effectively improve the beam training efficiency and has a small impact on the performance of the system.