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ORIGINAL RESEARCH article
Front. Commun. Netw.
Sec. Signal Processing for Communications
Volume 6 - 2025 | doi: 10.3389/frcmn.2025.1453125
Modulation recognition method based on multimodal features
Provisionally accepted- Xidian University, Xi'an, China
Automatic modulation recognition (AMR) plays a crucial role in modern communication systems for efficient signal processing and monitoring. However, existing modulation recognition methods often lack comprehensive feature extraction and suffer from recognition inaccuracies. To overcome these challenges, we present a multi-task modulation recognition approach leveraging multimodal features. In this method, a network is proposed to differentiate between multi-domain features for temporal feature extraction. Simultaneously, a network capable of extracting features at multiple scales is utilized for image feature extraction. Subsequently, recognition is conducted by integrating the multimodal features. Due to the inherent differences between 1D signal features and 2D image features, recognizing them collectively may overlook the unique characteristics of each type. Therefore, we propose a multi-task modulation recognition approach leveraging multimodal features to enhance accuracy. By integrating temporal and image-based feature extraction, our method outperforms existing techniques in recognition performance.
Keywords: Automatic modulation recognition, feature extraction, multi-domain, multi-task, deep learning
Received: 13 Aug 2024; Accepted: 10 Feb 2025.
Copyright: © 2025 Zhang, Kuang, Huang, Lin, Dong and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Sheng Lin, Xidian University, Xi'an, China
Min Zhang, Xidian University, Xi'an, China
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