Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network


Because an exhaustive search among all candidate beam pairs cannot be assuredly completed within short contact times, beam selection for millimeter-wave links in a vehicular scenario is a challenging problem. Our innovative expediting beam selection makes use of multimodal data obtained from sensors such as LiDAR, camera images, and GPS to solve this problem. Along with a study on the associated tradeoffs, we present individual modality and distributed fusion-based Deep Learning (F-DL) architectures that are capable of executing locally as well as at a mobile edge computing center (MEC). We also formulate and solve an optimization problem for determining the output dimensions of the aforementioned F-DL architectures. This problem takes into account the latency overheads associated with practical beam-searching, MEC processing, and sensor-to-MEC data delivery. The results of extensive evaluations performed on publicly available synthetic and home-grown real-world datasets reveal a 95% and 96% improvement in beam selection speed over traditional RF-only beam sweeping, respectively. These results were obtained by conducting the evaluations on publicly available datasets. F-DL also outperforms the techniques that are considered to be state-of-the-art by 20-22% when it comes to predicting the top-10 best beam pairs.

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