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README.md
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# DeepCSIv2
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This is the implementation of the INFOCOM 25 Workshop's (DeepWireless 25) paper-- DeepCSIv2:[Radio Fingerprinting of Wi-Fi Devices Through
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MIMO Compressed Channel Feedback](https://ieeexplore.ieee.org/abstract/document/11152893)
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<br/>
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### We present DeepCSIv2, a data-driven radio fingerprinting (RFP) algorithm to characterize Wi-Fi devices acting as stations (STAs) at the physical layer. DeepCSIv2 is based on a neural network architecture that automatically extracts the STA’s radio fingerprint from the feedback captured over the air and identifies the device.
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<p align="center">
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<img src="Images/DeepCSI-overview.png"
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alt="Markdown Monster icon"
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style="float: center;" />
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</p>
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If you find the project useful and you use this code, please cite our paper:
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<br/>
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e.g., python learning_test.py ./dataset/ 3 finger_rev_ 4 2 0,1,2,3 0 160 attention _ S1
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#### For any question or query, please contact [Foysal Haque](https://kfoysalhaque.github.io/) at _**haque.k@northeastern.edu**
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license: gpl-3.0
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---
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# DeepCSIv2
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This is the implementation of the INFOCOM 25 Workshop's (DeepWireless 25) paper-- DeepCSIv2:[Radio Fingerprinting of Wi-Fi Devices Through
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MIMO Compressed Channel Feedback](https://ieeexplore.ieee.org/abstract/document/11152893)
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<br/>
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### We present DeepCSIv2, a data-driven radio fingerprinting (RFP) algorithm to characterize Wi-Fi devices acting as stations (STAs) at the physical layer. DeepCSIv2 is based on a neural network architecture that automatically extracts the STA’s radio fingerprint from the feedback captured over the air and identifies the device.
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If you find the project useful and you use this code, please cite our paper:
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<br/>
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e.g., python learning_test.py ./dataset/ 3 finger_rev_ 4 2 0,1,2,3 0 160 attention _ S1
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#### For any question or query, please contact [Foysal Haque](https://kfoysalhaque.github.io/) at _**haque.k@northeastern.edu**
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