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.

PyTorch Implementation Modulation Recognition Networks on the RadioML2016 Dataset

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

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

An implementation of "Convolutional Radio Modulation Recognition Networks"

Using CNNs to correct distortion in modulation recognition

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