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liu2022predicting

Shuangrui Liu, "Predicting V2X mmWave Link-State at Intersections," Master's Thesis, School of Electrical Engineering and Computer Science (EECS), TU Berlin (TUB), January 2022. (Advisor: Max Schettler; Referees: Falko Dressler and Thomas Sikora)

Abstract

Millimeter-wave (mmWave) communication, as one of the crucial members of the 5th Generation Mobile Networks (5G), is considered a possible enabler to alleviate communication capacity limitations and a booster for cooperative perception. However, the sensitivity of its signals to obstacles is a major challenge in mobile mmWave communication networks. The sudden appearance of obstacles in line-of-sight (LOS) mmWave communication usually leads to interruption of transmission, which seriously affects the reliability and stability of the communication system, especially at intersections. In this thesis, I propose a prediction algorithm based on Dedicated Short-range Communication (DSRC), which uses the vehicle information transmitted by DSRC to predict the obstacles that may impede mmWave LOS communication in the following short period. This allows the vehicle to switch the mmWave communication link in advance of LOS communication blockage. To evaluate the performance of this system, I construct a simulation system linking mmWave communication with DSRC. Software experiments show that the proposed algorithm has a over 75% success rate in predicting obstacles that impede mmWave communication in the next 1s. It can effectively reduce the possibility of mmWave communication failure, thus enhancing the reliability and stability of the mmWave communication system.

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Shuangrui Liu

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@phdthesis{liu2022predicting,
    author = {Liu, Shuangrui},
    title = {{Predicting V2X mmWave Link-State at Intersections}},
    advisor = {Schettler, Max},
    institution = {School of Electrical Engineering and Computer Science (EECS)},
    location = {Berlin, Germany},
    month = {1},
    referee = {Dressler, Falko and Sikora, Thomas},
    school = {TU Berlin (TUB)},
    type = {Master's Thesis},
    year = {2022},
   }
   
   

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