LOFT: Latent Optimal Transport-guided Feature Transformation for Robust Automatic Modulation Recognition

Automatic Modulation Recognition (AMR) seeks to infer the modulation schemes of received signals without prior knowledge. While deep learning has greatly advanced AMR, severe noise in low signal-to-noise ratio (SNR) regimes corrupts learned feature representations, thereby degrading recognition performance. Unlike previous methods that focus on data augmentation or signal denoising, this paper investigates low-SNR AMR failures through the lens of shortcut learning. As SNR decreases, models tend to take shortcuts by collapsing their predictions onto a few dominant classes, leading to class confusion. To tackle this issue, we propose **L**atent **O**ptimal Transport-guided **F**eature **T**ransformation (**LOFT**), a plug-and-play framework for reversing feature collapse. Motivated by the semantic invariance of modulation classes across SNRs, LOFT constructs clean class prototypes as semantic anchors and formulates latent recovery through an optimal transport-guided alignment objective. A lightweight transport adapter is then trained to predict feature displacements that move degraded representations toward clean semantic anchors in a single step. Furthermore, LOFT utilizes confidence-aware routing to selectively activate latent correction without requiring SNR information during inference. Extensive experiments on benchmark datasets demonstrate that LOFT consistently improves AMR performance, yielding an average accuracy gain of 1.63% across all SNR conditions and a 3.04% improvement in severe noise conditions. Code will be released upon acceptance.