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Found 5 papers, 2 papers with code
Learning from the Best: Active Learning for Wireless Communications
We evaluate the performance of different active learning algorithms on a publicly available multi-modal dataset with different modalities including image and LiDAR.
Multiverse at the Edge: Interacting Real World and Digital Twins for Wireless Beamforming
To achieve this, we go beyond instantiating a single twin and propose the 'Multiverse' paradigm, with several possible digital twins attempting to capture the real world at different levels of fidelity.
Going Beyond RF: How AI-enabled Multimodal Beamforming will Shape the NextG Standard
Incorporating artificial intelligence and machine learning (AI/ML) methods within the 5G wireless standard promises autonomous network behavior and ultra-low-latency reconfiguration.
Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network
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.
PRONTO: Preamble Overhead Reduction with Neural Networks for Coarse Synchronization
The L-STF occupies upto 40% of the preamble length and takes upto 32 us of airtime.
