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The Ethereal
Unsupervised Representation Learning of Structured Radio Communication Signals
April 24, 2016 ยท Declared Dead ยท ๐ International Workshop on Sensing, Processing and Learning for Intelligent Machines
"No code URL or promise found in abstract"
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Authors
Timothy J. O'Shea, Johnathan Corgan, T. Charles Clancy
arXiv ID
1604.07078
Category
cs.LG: Machine Learning
Citations
90
Venue
International Workshop on Sensing, Processing and Learning for Intelligent Machines
Last Checked
3 months ago
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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