Beam Training in mmWave Vehicular Systems: Machine Learning for Decoupling Beam Selection
<|reference_start|>Beam Training in mmWave Vehicular Systems: Machine Learning for Decoupling Beam Selection: Codebook-based beam selection is one approach for configuring millimeter wave communication links. The overhead required to reconfigure the transmit and receive beam pair, though, increases in highly dynamic vehicular communication systems. Location information coupled with machine learning (ML) beam recommendation is one way to reduce the overhead of beam pair selection. In this paper, we develop ML-based location-aided approaches to decouple the beam selection between the user equipment (UE) and the base station (BS). We quantify the performance gaps due to decoupling beam selection and also disaggregating the UE's location information from the BS. Our simulation results show that decoupling beam selection with available location information at the BS performs comparable to joint beam pair selection at the BS. Moreover, decoupled beam selection without location closely approaches the performance of beam pair selection at the BS when sufficient beam pairs are swept.<|reference_end|>
@article{kilinc2024beam,
    title={Beam Training in mmWave Vehicular Systems: Machine Learning for
  Decoupling Beam Selection},
    author={Ibrahim Kilinc, Ryan M. Dreifuerst, Junghoon Kim, and Robert W. Heath
  Jr},
    journal={arXiv preprint arXiv:2404.10936},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.10936},
    primaryClass={eess.SP cs.LG}
}