5G-Based Passive Radar Sensing for Human Activity Recognition Using Deep Learning

Wireless sensing and human activity recognition is an important research direction for next-generation wire-less communications systems as they evolve towards Integrated Sensing and Communications (ISAC) using a single waveform. Although gesture recognition is a well-studied field in WiFi systems, relatively few works have exploited the use of the Fifth Generation (5G) New Radio (NR) waveform for sensing and activity recognition. This paper explores the utilization of 5G NR as an illuminator of opportunity to perform passive radar detection and activity recognition. It proposes leveraging the synchronization signal block (SSB) signal of 5G, which is emitted periodically, for processing in passive coherent location (PCL) systems. We develop a signal processing pipeline involving processing of the SSBs that are transmitted at regular intervals to extract the Channel State Information (CSI) between the transmitter and the receiver. The CSI data which provides the characteristics of the radio channel is fed into a Deep Learning (DL) model to discern specific gestures. Experimental results with an over-the-air 5G signal collection campaign show excellent classification results across five categories of activities.

无线感知与人类活动识别是下一代无线通信系统的一个重要研究方向，因为它们朝着使用单一波形的集成感知与通信（ISAC）方向发展。尽管手势识别在WiFi系统中是一个研究得较为深入的领域，但利用第五代（5G）新空口（NR）波形进行感知和活动识别的工作相对较少。本文探讨了利用5G NR作为机会照射源来进行无源雷达探测和活动识别。它提出利用5G周期性发射的同步信号块（SSB）信号，在无源相干定位（PCL）系统中进行处理。我们开发了一个信号处理流程，包括对定期发射的SSB进行处理，以提取发射机和接收机之间的信道状态信息（CSI）。提供无线电信道特性的CSI数据被输入到一个深度学习（DL）模型中，以识别特定的手势。通过空中5G信号采集活动获得的实验结果表明，在五类活动中都取得了出色的分类结果。