Literature Database Entry

laskos2023mobile

Christos Laskos, "Mobile mmWave Beam Tracking using Deep Q-Network Learning," Master's Thesis, School of Electrical Engineering and Computer Science (EECS), TU Berlin (TUB), December 2023. (Advisors: Anatolij Zubow and Sigrid Dimce; Referees: Falko Dressler and Thomas Sikora)

Abstract

With the rise of millimeter-wave (mmWave) communication systems, accurate beam tracking is a requirement for reliable link performance, especially under mobility. In this thesis, we create a mmWave beam tracking system using a Deep Q-Network (DQN) that tracks a moving mobile station. As inputs to the DQN, we use the signal strength, speed, and direction of the mobile station. Using these simple inputs, the base station can track the mobile station along its path. Unlike purely simulation-based solutions, we use transfer learning to apply DQN beam tracking to a real-world scenario created in our lab. For the first time, we show that DQN-based approaches perform well in real-world scenarios, and with the usage of transfer learning, we achieve an increase in tracking performance and a decrease in training time compared to the conventional learning approach.

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Christos Laskos

BibTeX reference

@phdthesis{laskos2023mobile,
    author = {Laskos, Christos},
    title = {{Mobile mmWave Beam Tracking using Deep Q-Network Learning}},
    advisor = {Zubow, Anatolij and Dimce, Sigrid},
    institution = {School of Electrical Engineering and Computer Science (EECS)},
    location = {Berlin, Germany},
    month = {12},
    referee = {Dressler, Falko and Sikora, Thomas},
    school = {TU Berlin (TUB)},
    type = {Master's Thesis},
    year = {2023},
   }
   
   

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Last modified: 2024-05-04