We survey the latest advances in machine learning with deep neural networks by applying them to the task of radio modulation recognition. Results show that radio modulation recognition is not limited by network depth and further work should focus on improving learned synchronization and equalization. Advances in these areas will likely come from novel architectures designed for these tasks or through novel training methods.
@article{arxiv.1703.09197,
title = {Deep Architectures for Modulation Recognition},
author = {Nathan E West and Timothy J. O'Shea},
journal= {arXiv preprint arXiv:1703.09197},
year = {2017}
}
Comments
7 pages, 14 figures, to be published in proceedings of IEEE DySPAN 2017