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arXiv · 1604.07078

Unsupervised Representation Learning of Structured Radio Communication Signals

Timothy J. O'Shea·Johnathan Corgan·T. Charles Clancy
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Abstract

We explore unsupervised representation learning of radio communication signals in raw sampled time series representation. We demonstrate that we can learn modulation basis functions using convolutional autoencoders and visually recognize their relationship to the analytic bases used in digital communications. We also propose and evaluate quantitative met- rics for quality of encoding using domain relevant performance metrics.

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Timothy J. O'Shea, Johnathan Corgan, T. Charles Clancy. 2016-04-24. Unsupervised Representation Learning of Structured Radio Communication Signals. https://arxiv.org/abs/1604.07078

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