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arXiv · 2508.20193

Enhancing Automatic Modulation Recognition With a Reconstruction-Driven Vision Transformer Under Limited Labels

Hossein Ahmadi·Banafsheh Saffari·Sajjad Emdadi Mahdimahalleh·Mohammad Esmaeil Safari·Aria Ahmadi
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Abstract

Automatic modulation recognition (AMR) is critical for cognitive radio, spectrum monitoring, and secure wireless communication. However, existing solutions often rely on large labeled datasets or multi-stage training pipelines, which limit scalability and generalization in practice. We propose a unified Vision Transformer (ViT) framework that integrates supervised, self-supervised, and reconstruction objectives. The model combines a ViT encoder, a lightweight convolutional decoder, and a linear classifier; the reconstruction branch maps augmented signals back to their originals, anchoring the encoder to fine-grained I/Q structure. This strategy promotes robust, discriminative feature learning during pretraining, while partial label supervision in fine-tuning enables effective classification with limited labels. On the RML2018.01A dataset, our approach outperforms supervised CNN and ViT baselines in low-label regimes, approaches ResNet-level accuracy with only 15-20% labeled data, and maintains strong performance across varying SNR levels. Overall, the framework provides a simple, generalizable, and label-efficient solution for AMR.

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Hossein Ahmadi, Banafsheh Saffari, Sajjad Emdadi Mahdimahalleh, Mohammad Esmaeil Safari, Aria Ahmadi. 2025-08-27. Enhancing Automatic Modulation Recognition With a Reconstruction-Driven Vision Transformer Under Limited Labels. https://arxiv.org/abs/2508.20193

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