期刊
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS
卷 23, 期 2, 页码 995-1019出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/COMST.2021.3058333
关键词
Sensors; Radio frequency; Deep learning; Wireless sensor networks; Wireless communication; Wireless fidelity; Monitoring; Wireless sensing; deep learning; WiFi sensing; human sensing; activity recognition
资金
- Cisco Systems, Inc.
The application of deep learning has significantly enhanced radio frequency (RF) sensing technology, achieving high accuracy and discovering novel sensing phenomena. Various deep learning models and datasets have facilitated research in this field, pointing towards promising future directions and addressing current limitations.
While decade-long research has clearly demonstrated the vast potential of radio frequency (RF) for many human sensing tasks, scaling this technology to large scenarios remained problematic with conventional approaches. Recently, researchers have successfully applied deep learning to take radio-based sensing to a new level. Many different types of deep learning models have been proposed to achieve high sensing accuracy over a large population and activity set, as well as in unseen environments. Deep learning has also enabled detection of novel human sensing phenomena that were previously not possible. In this survey, we provide a comprehensive review and taxonomy of recent research efforts on deep learning based RF sensing. We also identify and compare several publicly released labeled RF sensing datasets that can facilitate such deep learning research. Finally, we summarize the lessons learned and discuss the current limitations and future directions of deep learning based RF sensing.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据