Radio Signal Modulation Recognition Method Based on Deep Learning Model Pruning
Abstract
:1. Introduction
2. Related Work
3. Problem Definition
4. The Proposed Approach
5. Experimental Analysis
5.1. The Effect of CNN Kernel Size
5.2. The Effect of LSTM Unit Size
5.3. The Effect of LSTM Layer Number
5.4. The Effect of Dropout Layer Type
5.5. The Effect of Batch Size
5.6. Comparison with Existing Models
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Kernel Size | Loss |
---|---|
1 × 2 | 1.2986183166503906 |
1 × 3 | 1.2517382701237996 |
1 × 4 | 1.2592326800028484 |
1 × 5 | 1.1835391124089558 |
2 × 2 | 1.1842063665390015 |
2 × 3 | 1.1451027790705364 |
2 × 4 | 1.1322169701258342 |
2 × 5 | 1.1263486941655476 |
3 × 3 | 1.1229949394861858 |
4 × 4 | 1.1136138439178467 |
Batch Size | Loss | Time Per Epoch (s) |
---|---|---|
64 | 1.153881827990214 | 26 |
128 | 1.1463038523991902 | 17 |
256 | 1.119464596112569 | 13 |
512 | 1.1418431202570598 | 11 |
1024 | 1.1403136253356934 | 9 |
2048 | 1.1303218603134155 | 8 |
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Hao, X.; Xia, Z.; Jiang, M.; Ye, Q.; Yang, G. Radio Signal Modulation Recognition Method Based on Deep Learning Model Pruning. Appl. Sci. 2022, 12, 9894. https://doi.org/10.3390/app12199894
Hao X, Xia Z, Jiang M, Ye Q, Yang G. Radio Signal Modulation Recognition Method Based on Deep Learning Model Pruning. Applied Sciences. 2022; 12(19):9894. https://doi.org/10.3390/app12199894
Chicago/Turabian StyleHao, Xinyu, Zhang Xia, Mengxi Jiang, Qiubo Ye, and Guangsong Yang. 2022. "Radio Signal Modulation Recognition Method Based on Deep Learning Model Pruning" Applied Sciences 12, no. 19: 9894. https://doi.org/10.3390/app12199894
APA StyleHao, X., Xia, Z., Jiang, M., Ye, Q., & Yang, G. (2022). Radio Signal Modulation Recognition Method Based on Deep Learning Model Pruning. Applied Sciences, 12(19), 9894. https://doi.org/10.3390/app12199894