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IEEE/ICACT20260314 Slide.04        [Big Slide]       [YouTube]	Oral Presentation
	This slide summarizes the main contributions of our work. First, we propose a CNN–Transformer hybrid architecture tailored for I/Q-based radio signal classification. Second, we perform a systematic comparison of three attention mechanisms: baseline multi-head attention, causal attention, and sparse attention. Third, we evaluate not only classification accuracy but also computational efficiency, which is crucial for real-time applications. Finally, we provide a detailed per-modulation analysis to understand how different modulation types benefit from different attention patterns.

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