Name
Title | ||
|---|---|---|
ConvLSTMAE: A Spatiotemporal Parallel Autoencoders for Automatic Modulation Classification |
Abstract | ||
|---|---|---|
Automatic modulation classification (AMC) is the key technique in both military and civilian wireless communication. However, the performance is unsatisfactory, even several deep learning-based methods are involved. Targeting its low accuracy at low SNR, high computational cost and label overdependence, we propose a novel AMC framework, where the autoencoder (AE) serves as the backbone and Convolution-AE and LSTM-AE are combined in a parallel way as temporal and spatial feature extractors. The comparisons with serval algorithms on the radioML2016.10a show that our proposed network can achieve higher classification accuracy at low SNR with a low cost. In addition, it suits the semi-supervised scenario since the dependence on labels is loosen. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LCOMM.2022.3179003 | IEEE Communications Letters |
Keywords | DocType | Volume |
Automatic modulation classification,autoencoder,convolution,LSTM,semi-supervised | Journal | 26 |
Issue | ISSN | Citations |
8 | 1089-7798 | 0 |
PageRank | References | Authors |
0.34 | 14 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shi Yunhao | 1 | 0 | 0.34 |
| Xu Hua | 2 | 0 | 0.34 |
| Lei Jiang | 3 | 3 | 4.13 |
| Qi Zisen | 4 | 0 | 0.34 |