# DeepCSI: Rethinking Wi-Fi Radio Fingerprinting Through MU-MIMO CSI Feedback Deep Learning

arXiv: https://arxiv.org/abs/2204.07614 · HTML: https://mlsanity.com/paper/2204.07614/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 15 Apr 2022.

## Builds on (most cited first, 10 of 10)

- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [Over the Air Deep Learning Based Radio Signal Classification](https://mlsanity.com/paper/1712.04578/) (2017)
- [ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks](https://mlsanity.com/paper/1812.01124/) (2018)
- [Device Fingerprinting in Wireless Networks: Challenges and Opportunities](https://mlsanity.com/paper/1501.01367/) (2015)
- [DeepRadioID: Real-Time Channel-Resilient Optimization of Deep Learning-based Radio Fingerprinting Algorithms](https://mlsanity.com/paper/1904.07623/) (2019)
- [Robust Wireless Fingerprinting via Complex-Valued Neural Networks](https://mlsanity.com/paper/1905.09388/) (2019)
- [FID: Function Modeling-based Data-Independent and Channel-Robust Physical-Layer Identification](https://mlsanity.com/paper/1901.05914/) (2019)
- [IEEE 802.11be-Wi-Fi 7: New Challenges and Opportunities](https://mlsanity.com/paper/2007.13401/) (2020)
- [Can You Fix My Neural Network? Real-Time Adaptive Waveform Synthesis for Resilient Wireless Signal Classification](https://mlsanity.com/paper/2103.03745/) (2021)

## Built on by (most cited first, 1 of 1)

- [SoK: Security Evaluation of Wi-Fi CSI Biometrics: Attacks, Metrics, and Open Challenges](https://mlsanity.com/paper/2511.11381/) (2025)

## Abstract (arXiv)

We present DeepCSI, a novel approach to Wi-Fi radio fingerprinting (RFP) which leverages standard-compliant beamforming feedback matrices to authenticate MU-MIMO Wi-Fi devices on the move. By capturing unique imperfections in off-the-shelf radio circuitry, RFP techniques can identify wireless devices directly at the physical layer, allowing low-latency low-energy cryptography-free authentication. However, existing Wi-Fi RFP techniques are based on software-defined radio (SDRs), which may ultimately prevent their widespread adoption. Moreover, it is unclear whether existing strategies can work in the presence of MU-MIMO transmitters - a key technology in modern Wi-Fi standards. Conversely from prior work, DeepCSI does not require SDR technologies and can be run on any low-cost Wi-Fi device to authenticate MU-MIMO transmitters. Our key intuition is that imperfections in the transmitter's radio circuitry percolate onto the beamforming feedback matrix, and thus RFP can be performed without explicit channel state information (CSI) computation. DeepCSI is robust to inter-stream and inter-user interference being the beamforming feedback not affected by those phenomena. We extensively evaluate the performance of DeepCSI through a massive data collection campaign performed in the wild with off-the-shelf equipment, where 10 MU-MIMO Wi-Fi radios emit signals in different positions. Experimental results indicate that DeepCSI correctly identifies the transmitter with an accuracy of up to 98%. The identification accuracy remains above 82% when the device moves within the environment. To allow replicability and provide a performance benchmark, we pledge to share the 800 GB datasets - collected in static and, for the first time, dynamic conditions - and the code database with the community.