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

Name		Name	Last commit message	Last commit date
Latest commit History 16 Commits
__pycache__		__pycache__
Baseline_LSTM.ipynb		Baseline_LSTM.ipynb
Conv_layer_visualisations.ipynb		Conv_layer_visualisations.ipynb
Convolutional Radio Modulation Networks.pdf		Convolutional Radio Modulation Networks.pdf
Deep Learning Models for Wireless Signal Classification with Distributed Low-Cost Spectrum Sensors.pdf		Deep Learning Models for Wireless Signal Classification with Distributed Low-Cost Spectrum Sensors.pdf
EE5611 ML in wireless comm project presentation.pdf		EE5611 ML in wireless comm project presentation.pdf
EE5611_project_confusion_matrices.xlsx		EE5611_project_confusion_matrices.xlsx
EE5611_project_report.pdf		EE5611_project_report.pdf
README.md		README.md
Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification.pdf		Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification.pdf
baseline_cnn.ipynb		baseline_cnn.ipynb
baseline_scrnn.ipynb		baseline_scrnn.ipynb
dataset_conversion.ipynb		dataset_conversion.ipynb
model_architectures.py		model_architectures.py
new_SCRNNs.ipynb		new_SCRNNs.ipynb
new_lstm.ipynb		new_lstm.ipynb
plots.ipynb		plots.ipynb
scrnn_highsnr.html		scrnn_highsnr.html
scrnn_highsnr.ipynb		scrnn_highsnr.ipynb
scrnn_lowsnr.html		scrnn_lowsnr.html
scrnn_lowsnr.ipynb		scrnn_lowsnr.ipynb
utils.py		utils.py

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EE5611_project

DEEPSIG DATASET: RADIOML 2016.10A

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