A Dynamic Branch Automatic Modulation Recognition Method for Heterogeneous Data-Driven
To address insufficient feature complementarity mining and limited recognition accuracy in end-to-end deep learning models under complex channel environments, this paper proposes a dynamic branch automatic modulation recognition method driven by heterogeneous data. A multi-modal parallel feature ext...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11119632/ |
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