Name
Abstract | ||
|---|---|---|
In this paper, to improve the modulation recognition accuracy when the signal to noise ratio (SNR) is low, we propose a Wiener filter preprocessing aided intelligent modulation recognition method. In this design, the Wiener filter preprocessing is firstly conducted to reduce the noise of the received signal, then the signal cycle spectrum is calculated as the input to the deep neural network (DNN). Subsequently, the DNN classifier will extract the features of signals to recognize the modulation type. Simulation results show that when SNR is as low as -25dB, the average recognition accuracy of the proposed scheme is improved by about 40% compared with that of the scheme without applying the noise suppressing preprocessing operations. Moreover, compared with the benchmark scheme, the proposed scheme has achieved higher recognition accuracy in low SNR regions. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ICCC55456.2022.9880789 | 2022 IEEE/CIC International Conference on Communications in China (ICCC) |
Keywords | DocType | ISSN |
Wiener filter,cyclic spectral(CS) profiles,deep neural network(DNN),modulation recognition(MR) | Conference | 2377-8644 |
ISBN | Citations | PageRank |
978-1-6654-8481-7 | 0 | 0.34 |
References | Authors | |
3 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingreng Lei | 1 | 0 | 0.34 |
| Juncheng Zhou | 2 | 0 | 0.34 |
| Zijun Jian | 3 | 0 | 0.34 |
| Heng Liu | 4 | 153 | 27.10 |
| Lin Zhang | 5 | 38 | 22.81 |
| Yan Feng | 6 | 0 | 1.01 |
| Zhiqiang Wu | 7 | 0 | 0.68 |