Radio modulation recognition with CNN, CLDNN, CGDNN and MCTransformer architectures. Best results were achieved with the CGDNN architecture, which has roughly 50,000 parameters, and the final model has a memory footprint of 636kB. More details can be found in my bachelor thesis linked in the readme file.

MCNet: An Efficient CNN Architecture for Robust Automatic Modulation Classification

This is my Master's Degree Thesis repository. It's about Signal Modulation Classification (or Automatic Modulation Recognition) using Deep Learning.

CNN models to solve Automatic Modulation Classification problem.

Package to train and run modulation recognition on raw I/Q radio samples, via deep-learning models

This repository presents the code for digital modulation detection in Communication networks

This is an assignment for Pattern Recognition Course taught at Alexandria University, Faculty of Engineering offered in Spring 2019. The assignment goal is to design neural network that are able to classify the signals in the DeepSig dataset into their different modulation types.

This is the Matlab code for the paper "Denoising Higher-Order Moments for Blind Digital Modulation Identification in Multiple-Antenna Systems" published in the IEEE Wireless Communications Letters.

Automatic modulation recognition of DVB-S2X standard-specific with an APSK-based neural network classifier

Algorithm in Python 2.7 for amplitude, frequency, bandwidth and modulation identification of a signal

Official code for "EMC²-Net: Joint Equalization and Modulation Classification based on Constellation Network", ICASSP 2023.

This work home of the PO-233 discipline at ITA shows how to use a machine learning for digital modulation classification.

This project was for the pattern recognition course I studied in college. This project's aim was to classify the type of each modulation technique used using CNN, RNN, LSTM and CONV-LSTM.

Source code for lightning-fast modulation classification with hardware-efficient neural networks (ITU-ML5G-PS-007)

Modulation based classification for multi-spectral satellite images

CNN models to solve Automatic Modulation Classification problem.

This project automatically predict the modulation scheme of received RF signal without any prior information

This is the official repository of the paper "DNCNet: Deep Radar Signal Denoising and Recognition" from IEEE Transactions on Aerospace and Electronic Systems (TAES).

In this project, we have developed a basic CNN model which is used for "Automatic Modulation Classification" using constellation diagrams. Also we have experimented and compared the results obtained from both constellation diagrams and gray images.