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IEEE/ICACT20260314 Slide.17        [Big Slide]       [YouTube]	Oral Presentation
	In conclusion, this study presented a comparative analysis of attention mechanisms for automatic modulation classification. The proposed CNN–Transformer hybrid architecture achieves strong performance on the RML2016.10a dataset. While baseline attention provides the highest accuracy, causal and sparse attention offer substantial reductions in inference time with only minor performance loss. These findings provide useful guidance for designing efficient AMC systems in next-generation wireless networks. Thank you for your attention.

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