We study the adaptation of convolutional neural networks to the complex-valued temporal radio signal domain. We compare the efficacy of radio modulation classification using naively learned features against using expert feature based methods which are widely used today and e show significant performance improvements. We show that blind temporal learning on large and densely encoded time series using deep convolutional neural networks is viable and a strong candidate approach for this task especially at low signal to noise ratio.
CITATION STYLE
O’Shea, T. J., Corgan, J., & Clancy, T. C. (2016). Convolutional radio modulation recognition networks. In Communications in Computer and Information Science (Vol. 629, pp. 213–226). Springer Verlag. https://doi.org/10.1007/978-3-319-44188-7_16
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