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Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network. (arXiv:2201.04712v1 [cs.LG])
Jan. 14, 2022, 2:10 a.m. | Batool Salehi, Guillem Reus-Muns, Debashri Roy, Zifeng Wang, Tong Jian, Jennifer Dy, Stratis Ioannidis, Kaushik Chowdhury
cs.LG updates on arXiv.org arxiv.org
Beam selection for millimeter-wave links in a vehicular scenario is a
challenging problem, as an exhaustive search among all candidate beam pairs
cannot be assuredly completed within short contact times. We solve this problem
via a novel expediting beam selection by leveraging multimodal data collected
from sensors like LiDAR, camera images, and GPS. We propose individual modality
and distributed fusion-based deep learning (F-DL) architectures that can
execute locally as well as at a mobile edge computing center (MEC), with a …
arxiv data deep learning edge learning multimodal network sensor
More from arxiv.org / cs.LG updates on arXiv.org
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