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Automatic Modulation Classification, or AMC, is a critical component in modern wireless communication systems, particularly for cognitive radio, spectrum monitoring, and interference management.
Traditional feature-based approaches rely on handcrafted statistical features and often suffer from poor generalization under varying channel conditions.
Deep learning methods, especially convolutional neural networks, have significantly improved AMC performance.
However, CNNs mainly capture local patterns and are limited in modeling long-range temporal dependencies present in radio signals.
Attention mechanisms, originally introduced in Transformer architectures, provide an effective way to model such temporal dependencies.
This motivates our study on how different attention designs affect AMC performance and efficiency. |
IEEE/ICACT20260314 Slide.03
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