Deep Architectures for Modulation Recognition

27 Mar 2017  ·  Nathan E West, Timothy J. O'Shea ·

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. read more

PDF Abstract
No code implementations yet. Submit your code now



  Add Datasets introduced or used in this paper

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.


No methods listed for this paper. Add relevant methods here