LGMar 27, 2017
Deep Architectures for Modulation Recognition
arXiv:1703.09197v1495 citations
Originality Synthesis-oriented
AI Analysis
This is an incremental survey for researchers in signal processing and machine learning.
The paper surveys deep neural network applications for radio modulation recognition, finding that network depth is not a limiting factor and suggesting future work should focus on improving synchronization and equalization.
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.