# Thesis_Code_Automatic-Modulation-Classification

**Repository Path**: helloMRDJ/Thesis_Code_Automatic-Modulation-Classification

## Basic Information

- **Project Name**: Thesis_Code_Automatic-Modulation-Classification
- **Description**: Implementation of various Machine Learning Classifiers for my thesis 'Machine Learning Techniques for Automatic Modulation Classification'
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-04-02
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

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.