The aim of this project is to test feature generation pipeline using persistent homology with application to modulation classification problem.

The dataset consists of synthetic and over-the-air radio signals with different modulations. It was generated as a basis of the paper [1]. Signals with various modulation types were generated using the following schema

After cloning the repository set up virtual environment and install requirements

python -m venv venv 
source venv/bin/activate
pip install -r requirements.txt

This project uses DVC (Data Version Control) to run data processing pipelines. To download datasets set-up password to remote SSH storage, and pull the data:

dvc remote modify --local ssh-storage password [put_password_here]
dvc pull

Run the "spotcheck" stage to see the ranking of classifiers

dvc repro spotcheck

Run the "evaluate_model" stage to see performance of the best model

dvc repro evaluate_model

To see the accuracy of the best model and its confusion matrix run

dvc metrics show
dvc plots show

[1] Timothy James O'Shea, Tamoghna Roy, and T. Charles Clancy. "Over-the-air deep learning based radio signal classification." IEEE Journal of Selected Topics in Signal Processing, 12(1):168–179, February 2018

[2] Carlsson, Gunnar. "Topology and data." Bulletin of the American Mathematical Society 46.2 (2009): 255-308