Machine Learning Dataset for Radio Signal Classification

This RF signal dataset contains radio signals of 18 different waveforms for the training of machine learning systems. The dataset enables experiments on signal and modulation classification using modern machine learning such as deep learning with neural networks.

The data has been created synthetically by first modulating speech, music and text using standard software. Then the signals are cut into short slices. Each slice is impaired by Gaussian noise, Watterson fading (to account for ionospheric propagation) and random frequency and phase offset. This process generates data, that is close to real reception signals.

The RF signal dataset “Panoradio HF” has the following properties:

• 172,800 signal vectors
• Each signal vector has 2048 complex IQ samples with fs = 6 kHz (duration is 340 ms)
• The signals (resp. their actual bandwidths) are centered at 0 Hz (+- random frequency offset, see below)
• random frequency offset: +- 250 Hz
• random phase offset
• signal power is normalized to 1
• SNR values: 25, 20, 15, 10, 5, 0, -5, -10 dB (AWGN)
• fading channel: Watterson Model as defined by CCIR 520
• 18 Transmission Modes / Modulations (primarily appear in the HF band):

Some exemplary IQ signals of different type, different SNR (Gaussian) and different frequency offset

The RF signal dataset “Panoradio HF” is available for download in 2-D numpy array format with shape=(172800, 2048)

• dataset_panoradio_hf.zip.001
• dataset_panoradio_hf.zip.002
• dataset_panoradio_hf.zip.003
• dataset_panoradio_hf_tags.csv
• dataset_panoradio_hf_readme.txt

How to cite (bibtex):

@misc{scholl2019classificationradiosignalshf,
      title={Classification of Radio Signals and HF Transmission Modes with Deep Learning}, 
      author={Stefan Scholl},
      year={2019},
      eprint={1906.04459},
      archivePrefix={arXiv},
      primaryClass={eess.SP},
      url={https://arxiv.org/abs/1906.04459}, 
}

Related Publications:

• S. Scholl: Classification of Radio Signals and HF Transmission Modes with Deep Learning, 2019, https://arxiv.org/abs/1906.04459
• S. Scholl (DC9ST): Classification of shortwave radio signals with deep learning, Software Defined Radio Academy 2021