Name
Title | ||
|---|---|---|
Automatic Modulation Recognition Based on Adaptive Attention Mechanism and ResNeXt WSL Model |
Abstract | ||
|---|---|---|
Automatic modulation recognition (AMR) plays an important role in modern wireless communication. In this letter, a novel framework for AMR is proposed. The ResNeXt network serves as the backbone, and four proposed adaptive attention mechanism modules are incorporated. The time-frequency representations of the received signals are utilized as the inputs of the proposed deep learning (DL) network, a... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/LCOMM.2021.3093485 | IEEE Communications Letters |
Keywords | DocType | Volume |
Modulation,Feature extraction,Time-frequency analysis,Adaptive systems,Convolution,Spectrogram,Adaptation models | Journal | 25 |
Issue | ISSN | Citations |
9 | 1089-7798 | 2 |
PageRank | References | Authors |
0.37 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhi Liang | 1 | 2 | 0.71 |
| Mingliang Tao | 2 | 68 | 10.49 |
| Ling Wang | 3 | 14 | 6.76 |
| Jia Su | 4 | 35 | 10.65 |
| Xinyu Yang | 5 | 695 | 65.61 |