DeepSig has created a small corpus of standard datasets which can be used for original and reproduced research, experimentation, and measurement by fellow scientists and engineers.
These datasets allow machine learning researchers with new ideas to dive directly into an important technical area without the need for collecting or generating new datasets, and allows for direct comparison to efficacy of prior work.
DeepSig Dataset: RadioML 2016.10A
A synthetic dataset, generated with GNU Radio, consisting of 11 modulations (8 digital and 3 analog) at varying SNR levels. 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.
- Code to Generate: https://github.com/radioML/dataset
- Dataset Download: RML2016.10a_dict.dat.bz2
- Larger Version (including AM-SSB): RML2016.10b.dat.tar.bz2
- Example Classifier iPython/Jupyter Notebook: RML2016.10a_VTCNN2_example.ipynb
DeepSig Dataset: RadioML 2016.04C
A synthetic dataset, generated with GNU Radio, consisting of 11 modulations. This is a variable-SNR dataset with moderate LO drift, light fading, and numerous different labeled SNR increments for use in measuring performance across different signal and noise power scenarios.
This dataset was used for the "Convolutional Radio Modulation Recognition Networks" and "Unsupervised Representation Learning of Structured Radio Communications Signals" papers, found on our Publications Page.
There are three variations within this dataset with the following characteristics and labeling:
Dataset Download: 2016.04C.multisnr.pkl.bz2