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This research aims to develop a lightweight network that enhances automatic modulation recognition while maintaining computational efficiency.

May 29, 2026RadioengineeringOpen Access

TriFusion-Lite: A Temporal-Frequency-Phase Fusion Lightweight Network for Modulation Recognition

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This research aims to develop a lightweight network that enhances automatic modulation recognition while maintaining computational efficiency.
Introduced TriFusion-Lite, a multi-stream deep learning architecture for AMR.
Developed a preprocessing pipeline to compress input signals and enrich features.
Implemented a four-stream network including complex-valued convolutional and 1D convolutional streams, alongside an STFT stream.
Achieved competitive performance compared to state-of-the-art methods.
Demonstrated effective feature integration through a hierarchical fusion mechanism.
Showed balanced results across different compression levels, optimizing efficiency.

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Randomized trial demonstrates efficient modulation recognition in wireless communication, highlighting its potential for edge devices.

Cheng et al. (2026) studied this question.

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