Research on the Application of Deep Learning in Communication Signal Modulation Recognition and Blind Detection
Vol. 22 (2026): 2026 3rd International Conference on the Frontiers of Social Sciences, Education, and the Development of Humanities Arts (EDHA 2026)
Received: 2026-06-13
Accepted: 2026-06-13
Published: 2026-06-13
Abstract
With the rapid advancement of wireless communication technologies, communication signal environments have become increasingly complex. Traditional modulation recognition and blind detection methods based on manual feature extraction face numerous challenges when dealing with multipath fading, noise interference, and complex modulation schemes. Deep learning, with its powerful feature learning capabilities and adaptive modeling advantages, provides a new technical approach to address these challenges. This paper focuses on the application of deep learning in communication signal modulation recognition and blind detection. First, it outlines the research background, current status, and significance, systematically reviews the fundamental theories and common models of deep learning, and analyzes their applicability in the communication field. Based on this, the paper constructs deep learning-based modulation recognition models and blind detection strategies. Through model training optimization and experimental validation, the effectiveness of the proposed methods is demonstrated. Additionally, it explores challenges such as training data requirements, model complexity, and computational resource consumption during deep learning applications, proposing corresponding solutions. Finally, the paper discusses future prospects for integrating deep learning with emerging communication technologies, exploring new model algorithms, and expanding application domains, aiming to provide theoretical references and technical support for the further development of deep learning in communication signal processing.
Keywords
References
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Published in2026-06-13 16:02:02
DOI doi.org/10.70088/1kg4q680
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Copyright: © 2026 by the authors.
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Copyright © The Author(s), 2026. Published by EDHA 2026
Journal Information
- Vol. 22 (2026): 2026 3rd International Conference on the Frontiers of Social Sciences, Education, and the Development of Humanities Arts (EDHA 2026)
- 2026-06-13
- ISSN: (Print) 3078-770X/ (Online) 3078-7718
- Journal Homepage