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Analysis on dendritic deep learning model for AMR task
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DOI：10.1186/s42400-024-00306-9.png)
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摘要
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This study introduces a novel hybrid deep learning model featuring a dendritic layer for enhancing the performance of automatic modulation recognition (AMR). By replacing the fully connected layer, the proposed model demonstrates superior classification accuracy in AMR tasks. Comparative experiments with nine state-of-the-art deep learning models on the RadioML2016.10a dataset reveal its consistent superiority. Statistical analyses, including the Friedman test and Wilcoxon signed-rank test, confirm the significant advantage of the HDM-D model.
Keyword:
Spectrum sensing
Deep learning
Dendritic learning
Automatic modulation recognition
Communication security
期刊
C
IF:
3.7
论文数:
589
被引数:
1.0K
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