README

Welcome to the Radio Frequency Fingerprinting Recognition of WLAN Routers Using Convolutional Networks Project! This supporting material is organized into two main parts: one for generating WiFi data frames using GNURadio, and another for training neural networks using the generated data.

Project Structure

1. GRC Files for GNURadio (grcLTF Folder)

This folder contains two essential GRC files for generating WiFi data frames:

• wifi_loopback.grc
• wifi_phy_hier.grc

How to Use:

1. Initial Setup: If this is your first time running the project, you need to set the file path for data collection in the file sink block located after the WiFi Sync Long block in the wifi_phy_hier.grc file. Set the path to the ori_data folder as follows: '/home/buan/Supporting Material/LeNet/initial_data/ori_data/'+phase_noise_string+'_'+fre_offset_string+'_'+DC_offset_string
2. Run the Scripts: Once the path is correctly set, open and run the wifi_loopback.grc file in GNURadio Companion to start collecting WiFi data frames.

2. Neural Network Training Files

This section contains scripts for training neural networks, including LeNet and GoogLeNet. The process for both models is similar; however, the instructions below focus on LeNet as an example.

Steps to Train the Neural Network:

1. Data Extraction:

• Run extract.py to extract LTF OFDM symbols from the collected data frames stored in the ori_data folder under initial_data.
• The extracted data will be saved in the extracted_data folder.

2. Data Partitioning:

• Run data_partitioning.py to split the dataset into training and testing sets.
• The partitioned data will be saved in the mode_data folder.

3. Model Training:

• Run model_train.py to start training the model. You can set the number of epochs and the path for the log file within this script.
• Example code:

  train_process = train_model_process(LeNet, train_data, val_data, num_epochs=50)
  log_file = open("b4_20.txt", "w")

4. Model Testing:

• After training, run model_test.py to validate the model using the testing set and obtain accuracy metrics.

Additional Files:

1. LTF Signal Plotting:

• The LTF_plot.py under initial_data contains scripts to plot the IQ signals and OFDM symbols of the LTF.

2. Result Visualization:

• The Result folder stores data results, with each subfolder corresponding to results from different convolutional or pooling layer changes.
• The view_complexNpy.py script can be used to view the extracted LTF OFDM symbols saved in .npy format.
• Use viewResult.py to visualize the training results for a specific round.
• The LeNetAverageResult.py in the Result folder contains visualizations of the aggregated results.

Contact Information

If you have any questions or need further assistance, feel free to reach out to me at: Email: b.gu@hss23.qmul.ac.uk

Thank you for using this project!