haozhao10 / thesis_code_automatic-modulation-classification

Implementation of various Machine Learning Classifiers for my thesis 'Machine Learning Techniques for Automatic Modulation Classification'

This repo contains the implementation of various Machine Learning classifiers to solve the task of Digital Modulation Classification. data_feature-engineering.ipynb does feature engineering on raw data- dataset taken from https://www.deepsig.io/datasets; contains 8 classes of digital modulation- '8PSK', 'BPSK', 'CPFSK', 'GFSK', 'PAM4', 'QAM16', 'QAM64', 'QPSK'.

Dependencies- Python v3.6.3, NumPy v1.14.0, TensorFlow v1.4.0, scikit-learn v0.19.1, matplotlib v2.1.0, xgboost v0.6

K Nearest Neighbors, Support Vector Classifiers, Decision Trees, Decision Tree Ensembles and Extreme Gradient Boosting were implemented using scikit-learn.

Deep Neural Networks (DNNs)- fully connected and Convolutional Neural Networks (CNNs) were implemented using TensorFlow.