Enabling AI-based Sensing with 5G Networks at the Edge

Abstract

AI-based sensing methods for emerging sensing modalities such as multi-spectral sensing and integrated communications and sensing (CIS) require a large amount of dedicated resources, such as data storage, frequency spectrum, hardware, processing, and energy consumption. In this industry-led talk, we demonstrate key enabling techniques to perform AI-based sensing at the edge. To do so, we first explore the capabilities/opportunities for AI-based sensing methods that use communication features extracted from a mmWave 5G standard-compliant system. As an example, we experimentally demonstrate the use of directional communication features extracted from ambient mm-Wave 5G signals to perform object classification over 230 scenes with 98% accuracy in an indoor environment with an inference time <6ms per scene. Then, we describe the key system components to enable such opportunities, including (i) intelligent data ingestion, (ii) limited spectrum usage, (iii) software-based synchronization for data storage, and (iv) the use of communication features to perform sensing tasks. Finally, we provide an outlook on the next steps for the deployment of these emerging capabilities, including the utilization of AI accelerators.