Literature Database Entry

laskos2024towards

Christos Laskos, Sigrid Dimce, Anatolij Zubow and Falko Dressler, "Towards Virtual to Real-world Transfer Learning for Mobile mmWave Beam Tracking," Proceedings of IEEE Global Communications Conference (GLOBECOM 2024), Cape Town, South Africa, December 2024. (to appear)

Abstract

Adaptive beamforming is an enabling technology for millimeter-wave-based wireless communication which is used by many standards like 3GPP NR and IEEE 802.11ay. Supporting user mobility is challenging as efficient beam tracking is required. Therefore, a variety of beam tracking techniques have been proposed, many of which are machine learning (ML)-based. However, ML approaches are often not of practical use due to the long and complex learning phase. In this paper, we show the feasibility of virtual to real-world transfer learning. Our solution significantly speeds up the learning process as the learning happens mostly in a simulated environment and requires only little additional learning in the real-world deployment. As proof-of-concept, we implemented and evaluated a low-complexity beam tracking based on deep Q-network (DQN) reinforcement learning. The results reveal a substantial speed-up by a factor of 3× using transfer learning.

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Christos Laskos
Sigrid Dimce
Anatolij Zubow
Falko Dressler

BibTeX reference

@inproceedings{laskos2024towards,
    author = {Laskos, Christos and Dimce, Sigrid and Zubow, Anatolij and Dressler, Falko},
    note = {to appear},
    title = {{Towards Virtual to Real-world Transfer Learning for Mobile mmWave Beam Tracking}},
    publisher = {IEEE},
    address = {Cape Town, South Africa},
    booktitle = {IEEE Global Communications Conference (GLOBECOM 2024)},
    month = {12},
    year = {2024},
   }
   
   

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Last modified: 2024-10-06