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Machine Learning for Wireless communications course project on Sequential Convolutional Recurrent Neural Networks for fast automatic modulation classification.

EE5611_project

Machine Learning for Wireless communications course project on Sequential Convolutional Recurrent Neural Networks for fast automatic modulation classification by kaisheng Liao, Guanhong Tao, Yi Zhong, Yaping Zhang, Zhenghong Zhang.

Link to the paper: https://arxiv.org/abs/1909.03050 The datasets for this project can be found at https://www.deepsig.io/datasets.

DEEPSIG DATASET: RADIOML 2016.10A

A synthetic dataset, generated with GNU Radio, consisting of 11 modulations (8 digital and 3 analog) at varying signal-to-noise ratios. This dataset was first released at the 6th Annual GNU Radio Conference.

This represents a cleaner and more normalized version of the 2016.04C dataset, which this supersedes. The file is formatted as a "pickle" file which can be open for example in python by using cPickle.load(...).

Signal Generation Software: https://github.com/radioML/dataset

Dataset Download: http://opendata.deepsig.io/datasets/2016.10/RML2016.10a.tar.bz2

Larger Version (including AM-SSB): http://opendata.deepsig.io/datasets/2016.10/RML2016.10b.tar.bz2

Example ClassifierJupyter Notebook: https://github.com/radioML/examples/blob/master/modulation_recognition/RML2016.10a_VTCNN2_example.ipynb