Angle-Agnostic Radio Frequency Sensing Integrated Into 5G-NR

Abstract

The fusion of radio frequency (RF) sensing with cellular communication networks presents a revolutionary paradigm, enabling networks to seamlessly integrate communication and perception capabilities. Leveraging electromagnetic radiation, this technology facilitates the detection and interpretation of human movements, activities, and environmental changes. This article proposes a novel implementation of RF sensing within the allocated resources for new radio (NR) sidelink direct device-to-device (D2D) communication, showcasing the synergy between RF sensing and machine-learning (ML) techniques. The article addresses the inherent challenge of angle dependency in the sidelink-enabled sensing scheme, and introduces innovative solutions to achieve angle-agnostic environmental perception. The proposed approach incorporates a graph-based encoding of movement and gesture sequences, capturing spatio-temporal relations, and integrates orientation tracking to enhance human gesture recognition. The proposed model surpasses state-of-the-art algorithms, demonstrating a remarkable 100% accuracy in RF sensing when all the angles are available. Although the performance of our proposed method does decline with fewer available angles, it demonstrates exceptional resilience to missing data. Specifically, our model significantly outperforms existing models by approximately 70% in scenarios where seven out of eight angles are unavailable. To further advance sensing capabilities in RF sensing systems, a comprehensive dataset comprising 15 subjects performing 21 gestures, recorded from eight different angles, is openly shared. This contribution aims to enhance the performance and reliability of RF sensing systems by providing a robust and efficient ML-driven solution for human gesture recognition within NR sidelink D2D communication networks, aligning with the latest advancements in ML for RF sensing applications.