Name
Abstract | ||
|---|---|---|
As the use of wireless communication expands demand for radio spectrum, so does the need for effective automatic modulation recognition (AMR). Current methods of AMR include feature extractions, maximum likelihood algorithms, and deep learning (DL) networks primarily based on CNN structures. Many methods are limited by the slow training and testing time, the need for massive amounts of training da... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/DySPAN53946.2021.9677368 | 2021 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN) |
Keywords | DocType | ISSN |
Deep Learning,Modulation Recognition,Neural Networks,Data Generation,Matched Filter,CNN,LSTM,Autoencoder,Fully Connected Network | Conference | 2334-3125 |
ISBN | Citations | PageRank |
978-1-6654-1339-8 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tina L. Burns | 1 | 0 | 0.34 |
| Richard P. Martin | 2 | 0 | 0.34 |
| Jorge Ortiz | 3 | 0 | 0.34 |
| Ivan Seskar | 4 | 0 | 1.01 |
| Dragoslav Stojadinovic | 5 | 0 | 1.35 |
| Ryan Davis | 6 | 0 | 0.34 |
| Miguel Camelo | 7 | 0 | 0.34 |