update README

README.md

@@ -1,3 +1,69 @@
----
-license: gpl-3.0
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+# DeepCSIv2
+This is the implementation of the INFOCOM 25 Workshop's (DeepWireless 25) paper-- DeepCSIv2:[Radio Fingerprinting of Wi-Fi Devices Through
+MIMO Compressed Channel Feedback](https://ieeexplore.ieee.org/abstract/document/11152893)
+
+
+<br/>
+
+### 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.
+<br/>
+
+<p align="center">
+<img src="Images/DeepCSI-overview.png"
+     alt="Markdown Monster icon"
+     style="float: center;" />
+</p>
+
+If you find the project useful and you use this code, please cite our paper:
+<br/>
+
+```
+
+@inproceedings{meneghello2025radio,
+  title={Radio Fingerprinting of Wi-Fi Devices Through MIMO Compressed Channel Feedback},
+  author={Meneghello, Francesca and Haque, Khandaker Foysal and Restuccia, Francesco},
+  booktitle={IEEE INFOCOM 2025-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)},
+  pages={1--6},
+  year={2025},
+  organization={IEEE}
+}
+
+```
+<br/>
+<br/>
+
+### Please [download the dataset](https://huggingface.co/datasets/foysalhaque/DeepCSIv2/tree/main) and keep in the ```DeepCSIv2/input_files``` directory. You can directly download the Vmatrices or download the raw traces and extract the Vmatrices. The extraction procedure is also provided.
+
+### Extract the beamforming feedback with [Wi-BFI Tool](https://github.com/kfoysalhaque/Wi-BFI)
+```bash
+python main_extract_batch.py '<trace_dir>' '<standard>' '<MIMO>' '<Antenna-config>' '<BW>' '<packets to process>' '<vmatrices_dir>' '<bfa_dir>'
+```
+Example: python main_extract_batch.py /data/pcap_traces AX MU 4x2 80 5000 /data/output/vmatrices /data/output/bfa
+
+### Beamforming feedback matrices dataset creation 
+The following script creates the datasets of beamforming feedback matrices.
+```bash
+python create_dataset.py <'directory of input data'> <'number of different positions'> <'maximum number of samples to consider'> <'prefix to identify the data'> <'folder to save the dataset'> <'select random indices (`rand`) or subsample the data (`sampling`)'>
+```
+e.g., 
+- python create_dataset.py ./input_files/ 3 6000 _ ./dataset/ sampling
+
+### Train the learning algorithm for fingerprinting and test the performance
+The following script allows training and testing DeepCSIv2 on the scenarios identified in the reference paper based on the arguments passed as input.
+The fingerprinting is beamformee-specific: one model has to be trained using the feedback matrices collected from each of the beamformees.
+```bash
+python learning.py <'directory of the beamforming feedback matrices dataset'>  <'number of different positions'> <'name for the model to be saved'> <'number of transmitter antennas'> <'number of receiver antennas'> <'indices of the transmitter antennas to consider, comma separated'> <'indices of the receiver antennas to consider, comma separated'> <'bandwidth'> <'model type in {`convolutional`, `attention`}'> <'prefix to identify the data'> <'scenario considered in {S1, S2, S3, S4}'>
+```
+e.g., 
+- python learning.py ./dataset/ 3 finger_ 4 2 0,1,2,3 0 160 attention _ S1
+- python learning.py ./dataset/ 3 finger_ 4 2 0,1,2,3 0 160 attention_hyper_selection-128,128,128,128-7,7,7,5 _ hyper
+
+
+### Test the performance of the algorithm already trained
+```bash
+python learning_test.py <'directory of the beamforming feedback matrices dataset'>  <'number of different positions'> <'name for the model to be saved'> <'number of transmitter antennas'> <'number of receiver antennas'> <'indices of the transmitter antennas to consider, comma separated'> <'indices of the receiver antennas to consider, comma separated'> <'bandwidth'> <'model type in {`convolutional`, `attention`}'> <'prefix to identify the data'> <'scenario considered in {S1, S2, S3, S4}'>
+```
+e.g., python learning_test.py ./dataset/ 3 finger_rev_ 4 2 0,1,2,3 0 160 attention _ S1
+
+
+#### For any question or query, please contact [Foysal Haque](https://kfoysalhaque.github.io/) at _**haque.k@northeastern.edu**