Abstract:
An exhaustive search of all candidate beam pairs for millimeter-wave links in a vehicular scenario cannot be completed within short contact times. We use LiDAR, camera, and GPS data to speed up beam selection.
We propose individual modality and distributed fusion-based deep learning (F-DL) architectures that can run locally and at a mobile edge computing center (MEC) and study their tradeoffs.
For F-DL architecture output dimensions, we formulate and solve an optimization problem that takes into account practical beam-searching, MEC processing, and sensor-to-MEC data delivery latency overheads.
Extensive evaluations on publicly available synthetic and home-grown real-world datasets show 95% and 96% beam selection speed improvements over classical RF-only beam sweeping. F-DL predicts top-10 beam pairs 20-22% better than state-of-the-art methods.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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