{"ID":2888714,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.09996","arxiv_id":"2508.09996","title":"Comparative Analysis of Attention Mechanisms for Automatic Modulation Classification in Radio Frequency Signals","abstract":"Automatic Modulation Classification (AMC) is a critical component in cognitive radio systems and spectrum management applications. This study presents a comprehensive comparative analysis of three attention mechanisms (i.e., baseline multi-head attention, causal attention, and sparse attention) integrated with Convolutional Neural Networks (CNNs) for radio frequency (RF) signal classification. It proposes a novel CNN-Transformer hybrid architecture that leverages different attention patterns to capture temporal dependencies in I/Q samples from the RML2016.10a dataset. The experimental results demonstrate that while baseline attention achieves the highest accuracy of 85.05\\%, causal and sparse attention mechanisms offer significant computational advantages with inference times reduced by 83\\% and 75\\% respectively, while maintaining competitive classification performance above 84\\%. The analysis reveals distinct attention pattern preferences across different modulation schemes, providing insights for designing efficient attention mechanisms for real-time radio signal processing applications.","short_abstract":"Automatic Modulation Classification (AMC) is a critical component in cognitive radio systems and spectrum management applications. This study presents a comprehensive comparative analysis of three attention mechanisms (i.e., baseline multi-head attention, causal attention, and sparse attention) integrated with Convolut...","url_abs":"https://arxiv.org/abs/2508.09996","url_pdf":"https://arxiv.org/pdf/2508.09996v1","authors":"[\"Ferhat Ozgur Catak\",\"Murat Kuzlu\",\"Umit Cali\"]","published":"2025-07-30T08:46:59Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Transformer\",\"Convolutional Neural Network\"]","has_code":false}