Thanks to the ubiquitous deployment of Wi-Fi hotspots, channel state information (CSI)-based Wi-Fi sensing can unleash game-changing applications in many fields, such as healthcare, security, and entertainment. However, despite one decade of active research on Wi-Fi sensing, most existing work only considers legacy IEEE 802.11n devices, often in particular and strictly-controlled environments. Worse yet, there is a fundamental lack of understanding of the impact on CSI-based sensing of modern Wi-Fi features, such as 160-MHz bandwidth, multiple-input multiple-output (MIMO) transmissions, and increased spectral resolution in IEEE 802.11ax (Wi-Fi 6). This work aims to shed light on the impact of Wi-Fi 6 features on the sensing performance and to create a benchmark for future research on Wi-Fi sensing. To this end, we perform an extensive CSI data collection campaign involving 3 individuals, 3 environments, and 12 activities, using Wi-Fi 6 signals. An anonymized ground truth obtained through video recording accompanies our 80-GB dataset, which contains almost two hours of CSI data from three collectors. We leverage our dataset to dissect the performance of a state-of-the-art sensing framework across different environments and individuals. Our key findings suggest that (i) MIMO transmissions and higher spectral resolution might be more beneficial than larger bandwidth for sensing applications; (ii) there is a pressing need to standardize research on Wi-Fi sensing because the path towards a truly environment-independent framework is still uncertain. To ease the experiments' replicability and address the current lack of Wi-Fi 6 CSI datasets, we release our 80-GB dataset to the community.
SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human Sensing
Nanyang Technological University（南洋理工大学）；University of Edinburgh（爱丁堡大学）；Institute for Infocomm Research（信息通信研究院）；Agency for Science, Technology and Research（新加坡科技研究局）
Openwifi CSI fuzzer for authorized sensing and covert channels
WiFi系统的CSI（Channel State Information，信道状态信息）包含了发射机和接收机之间的环境信道响应，因此可以感测到人/物体及其之间的移动。为了获得CSI，接收器基于所发送的WiFi信号的预先已知的训练字段来执行信道估计。CSI相关技术在许多情况下是有用的，但它也带来了对隐私和安全的担忧。在本文中，我们开源了CSI模糊器，以提高WiFi CSI应用程序的隐私和安全性。它内置并嵌入到openwifi的发射器中，openwifi是一种开源的全栈WiFi芯片设计，可以在不牺牲WiFi链路性能的情况下防止未经授权的感知。CSI模糊器在信号被发送之前对信号施加人工信道响应，因此接收器看到的CSI将指示与人工响应组合的实际信道响应。只有知道人工响应的授权接收机才能计算实际信道响应并执行CSI感测。CSI模糊器的另一个潜在应用是基于一组预定义的人工响应模式的隐蔽通道。我们的工作解决了基于商用现成WiFi设备实现反感知思想的痛点。
Beamforming Feedback-based Model-Driven Angle of Departure Estimation Toward Legacy Support in WiFi Sensing: An Experimental Study
Graduate School of Informatics, Kyoto University（京都大学信息学研究科）；School of Engineering, Tokyo Institute of Technology（东京工业大学工学院）；Centre of Wireless Communications, University of Oulu（奥卢大学无线通信中心）
Synergistic Localization and Sensing in MIMO-OFDM Systems via Mixed-Integer Bilevel Learning
Department of Computer Science and Technology, Tongji University（同济大学计算机科学与技术系）；Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Department of Electrical and Computer Engineering, National University of Singapore（新加坡国立大学电子与计算机工程系）
