Name
Title | ||
|---|---|---|
Real-Time OFDM Signal Modulation Classification Based on Deep Learning and Software-Defined Radio |
Abstract | ||
|---|---|---|
This letter presents our initial results for real-time orthogonal frequency division multiplexing (OFDM) signal modulation classification based on deep learning and software-defined radio. We generate a modulation classification dataset under a dynamic fading channel, including 6 different OFDM modulation signals, and propose a novel neural network with triple-skip residual stack (TRS) as the basi... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/LCOMM.2021.3093451 | IEEE Communications Letters |
Keywords | DocType | Volume |
OFDM,Real-time systems,Convolution,Fading channels,Deep learning,Signal to noise ratio,Payloads | Journal | 25 |
Issue | ISSN | Citations |
9 | 1089-7798 | 2 |
PageRank | References | Authors |
0.38 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Limin Zhang | 1 | 7 | 5.23 |
| Chong Lin | 2 | 2 | 0.38 |
| Wenjun Yan | 3 | 2 | 0.38 |
| Qing Ling | 4 | 2 | 0.72 |
| Yu Wang | 5 | 36 | 8.93 |