# ReWiS: Reliable Wi-Fi Sensing Through Few-Shot Multi-Antenna Multi-Receiver CSI Learning

arXiv: https://arxiv.org/abs/2201.00869 · HTML: https://mlsanity.com/paper/2201.00869/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2022.

## Built on by (most cited first, 9 of 9)

- [WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activity Sensing](https://mlsanity.com/paper/2402.09430/) (2024)
- [AutoFi: Towards Automatic WiFi Human Sensing via Geometric Self-Supervised Learning](https://mlsanity.com/paper/2205.01629/) (2022)
- [A Survey on Wi-Fi Sensing Generalizability: Taxonomy, Techniques, Datasets, and Future Research Prospects](https://mlsanity.com/paper/2503.08008/) (2025)
- [Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitations](https://mlsanity.com/paper/2302.00992/) (2023)
- [A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Channels](https://mlsanity.com/paper/2305.03170/) (2023)
- [Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing Data](https://mlsanity.com/paper/2407.04733/) (2024)
- [Neuro-Symbolic Fusion of Wi-Fi Sensing Data for Passive Radar with Inter-Modal Knowledge Transfer](https://mlsanity.com/paper/2407.04734/) (2024)
- [RSSI-Assisted CSI-Based Passenger Counting with Multiple Wi-Fi Receivers](https://mlsanity.com/paper/2410.11400/) (2024)
- [Towards Causally Interpretable Wi-Fi CSI-Based Human Activity Recognition with Discrete Latent Compression and LTL Rule Extraction](https://mlsanity.com/paper/2604.22979/) (2026)

## Abstract (arXiv)

Thanks to the ubiquitousness of Wi-Fi access points and devices, Wi-Fi sensing enables transformative applications in remote health care, security, and surveillance. Existing work has explored the usage of machine learning on channel state information (CSI) computed from Wi-Fi packets to classify events of interest. However, most of these algorithms require a significant amount of data collection, as well as extensive computational power for additional CSI feature extraction. Moreover, the majority of these models suffer from poor accuracy when tested in a new/untrained environment. In this paper, we propose ReWiS, a novel framework for robust and environment-independent Wi-Fi sensing. The key innovation of ReWiS is to leverage few-shot learning (FSL) as the inference engine, which (i) reduces the need for extensive data collection and application-specific feature extraction; (ii) can rapidly generalize to new tasks by leveraging only a few new samples. We prototype ReWiS using off-the-shelf Wi-Fi equipment and showcase its performance by considering a compelling use case of human activity recognition. Thus, we perform an extensive data collection campaign in three different propagation environments with two human subjects. We evaluate the impact of each diversity component on the performance and compare ReWiS with a traditional convolutional neural network (CNN) approach. Experimental results show that ReWiS improves the performance by about 40% with respect to existing single-antenna low-resolution approaches. Moreover, when compared to a CNN-based approach, ReWiS shows a 35% more accuracy and less than 10% drop in accuracy when tested in different environments, while the CNN drops by more than 45%.