Importance

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Abstract

The disclosed system and methodology enable coexistence of networking and sensing on next-generation millimeter-wave (mmWave) picocells for traffic monitoring and pedestrian safety at intersections in all weather conditions. Existing wireless signal-based object detection systems suffer from limited resolution, and their outputs may not provide sufficient discriminatory information in complex scenes, such as traffic intersections. The disclosed system uses 5G picocells, which operate at mmWave frequency bands and provide higher data rates and higher sensing resolution than traditional wireless technology. It is difficult to run sensing applications and data transfer simultaneously on mmWave devices due to potential interference. MmWave devices are vulnerable to weak reflectivity and specularity challenges which may result in loss of information about objects and pedestrians. To address such challenges, the disclosed design uses customized deep learning models that not only can recover missing information about the target scene but also enable coexistence of networking and sensing.

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First Claim