WiFi sensing based on channel state information (CSI) collected from commodity WiFi devices has shown great potential across a wide range of applications, including vital sign monitoring and indoor localization. Existing WiFi sensing approaches typically estimate motion information directly from CSI. However, they often overlook the inherent advantages of channel impulse response (CIR), a delay-domain representation that enables more intuitive and principled motion sensing by naturally concentrating motion energy and separating multipath components. Motivated by this, we revisit WiFi sensing and introduce CIRSense, a new framework that enhances the performance and interpretability of WiFi sensing with CIR. CIRSense is built upon a new motion model that characterizes fractional delay effects, a fundamental challenge in CIR-based sensing. This theoretical model underpins technical advances for the three challenges in WiFi sensing: hardware distortion compensation, high-resolution distance estimation, and subcarrier aggregation for extended range sensing. CIRSense, operating with a 160 MHz channel bandwidth, demonstrates versatile sensing capabilities through its dual-mode design, achieving a mean error of approximately 0.25 bpm in respiration monitoring and 0.09 m in distance estimation. Comprehensive evaluations across residential spaces, far-range scenarios, and multi-target settings demonstrate CIRSense's superior performance over state-of-the-art CSI-based baselines. Notably, at a challenging sensing distance of 20 m, CIRSense achieves at least$3\times$higher average accuracy with more than$4.5\times$higher computational efficiency.

This paper presents DeepCRF, a new framework that harnesses deep learning to extract subtle micro-signals from channel state information (CSI) measurements, enabling robust and resilient radio-frequency fingerprinting (RFF) of commercial-off-the-shelf (COTS) WiFi devices across diverse channel conditions. Building on our previous research, which demonstrated that micro-signals in CSI, termed micro-CSI, most likely originate from RF circuitry imperfections and can serve as unique RF fingerprints, we develop a new approach to overcome the limitations of our prior signal space-based method. While the signal space-based method is effective in strong line-of-sight (LoS) conditions, we show that it struggles with the complexities of non-line-of-sight (NLoS) scenarios, compromising the robustness of CSI-based RFF. To address this challenge, DeepCRF incorporates a carefully trained convolutional neural network (CNN) with model-inspired data augmentation, supervised contrastive learning, and decision fusion techniques, enhancing its generalization capabilities across unseen channel conditions and resilience against noise. Our evaluations demonstrate that DeepCRF significantly improves device identification accuracy across diverse channels, outperforming both the signal space-based baseline and state-of-the-art neural network-based benchmarks. Notably, it achieves an average identification accuracy of 99.53% among 19 COTS WiFi network interface cards in real-world unseen scenarios using 4 CSI measurements per identification procedure.

In numerous WiFi sensing applications, such as passive human localization, the precision of sensing is often influenced by the estimation accuracy of multipath parameters. Several existing algorithms leverage pulse-shaping filter information to enhance multipath and channel estimation. However, WiFi chips do not disclose this filter information, and no current research has focused on measuring or estimating these pulse-shaping filters. In this paper, we introduce a new deep learning approach for the accurate estimation of pulse-shaping filters using channel state information (CSI), which incorporates both multipath channel information and pulse-shaping filter information. Specifically, we construct a convolutional neural network consisting of an encoder-regressor architecture, where the encoder translates the CSI into a latent representation, and the regressor subsequently estimates the pulse-shaping filter from this representation. Our proposed model's efficacy is demonstrated through its low normalized root mean squared error (NRMSE) in a variety of channel conditions, highlighting its ability to accurately estimate pulse-shaping filters.

This paper introduces CSI-RFF, a new framework that leverages micro-signals embedded within Channel State Information (CSI) curves to realize Radio-Frequency Fingerprinting of commodity off-the-shelf (COTS) WiFi devices for open-set authentication. The micro-signals that serve as RF fingerprints are termed “micro-CSI”. Through experimentation, we have found that the presence of micro-CSI can primarily be attributed to imperfections in the RF circuitry. Furthermore, this characteristic signal is detectable in WiFi 4/5/6 network interface cards (NICs). We have conducted further experiments to determine the most effective CSI collection configurations to stabilize micro-CSI. Yet, extracting micro-CSI for authentication purposes poses a significant challenge. This complexity arises from the fact that CSI measurements inherently include both micro-CSI and the distortions introduced by wireless channels. These two elements are intricately intertwined, making their separation non-trivial. To tackle this challenge, we have developed a signal space-based extraction technique for line-of-sight (LoS) scenarios, which can effectively separate the distortions caused by wireless channels and micro-CSI. Over the course of our comprehensive CSI data collection period extending beyond one year, we found that the extracted micro-CSI displays unique characteristics specific to each WiFi device and remains invariant over time. This establishes micro-CSI as a suitable candidate for device fingerprinting. Finally, we conduct a case study focusing on area access control for mobile robots. In particular, we applied our CSI-RFF framework to identify mobile robots operating in real-world indoor LoS environments based on their transmitted WiFi signals. To accomplish this, we have compared and employed anomaly detection algorithms for the authentication of 15 COTS WiFi 4/5/6 NICs that were carried by a mobile robot under both static and mobile conditions, maintaining an average signal-to-noise ratio (SNR) of 34 dB. Our experimental results demonstrate that the micro-CSI-based authentication algorithm can achieve an average attack detection rate close to 99% with a false alarm rate of 0% in both static and mobile conditions when using 20 CSI measurements to construct one fingerprint.