In an arxiv pre-publication report out today, Johnathan Corgan and I study the adaptation of convolutional neural networks to the task of modulation recognition in wireless systems. We use a relatively simple two layer convolutional network followed by two dense layers, a much smaller network than required for tasks such as ImageNet/ILVC.
We demonstrate that blind time domain feature learning can perform extremely well at the task of modulation classification, achieving a very high accuracy rate on both clean and noisy data sets.
As we compare the classifier performance across a wide range of signal to noise ratios, we demonstrate that it outperforms a number of more traditional expert classifiers using zero-delay cumulant features by a large margin.
While this is preliminary work, we think the results are exciting and that many additional promising results will come from the marriage of software radio and deep learning fields.
For much more detail on these results, please see our paper! http://arxiv.org/abs/1602.04105