Virual Oral Presentation Slide by Slide

IEEE/ICACT20260314 Slide.06        [Big Slide]       [YouTube]	Oral Presentation
	The CNN–Transformer hybrid architecture consists of three main components. First, the CNN feature extractor uses one-dimensional convolutional layers with batch normalization and ReLU activation to extract local features from I/Q samples. Second, the Transformer block applies multi-head attention to model temporal dependencies across the signal sequence. Residual connections and layer normalization are used to stabilize training. Finally, a lightweight multilayer perceptron produces the final modulation classification output.

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