Modulation-Prior Enhanced Cross-Modal Graph Learning for Low-SNR Automatic Modulation Recognition

Automatic modulation recognition (AMR) is a key enabling technique for intelligent spectrum sensing, non-cooperative wireless signal analysis, and communication monitoring. However, its reliability degrades significantly under low signal-to-noise ratio (SNR) conditions, where noise weakens local I/Q waveform patterns, blurs time-frequency structures, and limits the robustness of single-domain representations. Although multimodal AMR methods have improved recognition by combining I/Q and time-frequency representations, many of them still use fixed fusion rules, which may not reflect the changing reliability of different modalities across SNR levels. In addition, most existing backbones model signal sequences in a Euclidean manner and lack an explicit mechanism to capture adaptive relational structures among signal tokens under strong noise. To address these limitations, this paper proposes MPCG-Net, a modulation-prior-enhanced cross-modal graph learning framework for low-SNR AMR. MPCG-Net first performs modulation-prior-enhanced cross-modal encoding, where learnable multi-resolution spectro-temporal representation learning enhances the input representation and training-stage SNR-aware cross-modal consistency alignment improves the complementary interaction between I/Q and spectro-temporal tokens. A modulation context memory module then refines the fused tokens before graph construction. In the graph-based relational learning stage, a temporal similarity graph with self-loops, local temporal-chain edges, and top-k feature-similarity edges is built to learn more discriminative graph-level signal representations. Experiments on RadioML2016.10A, RadioML2016.10B, and RML22 validate the effectiveness of the proposed design. Compared with the strongest baseline on each dataset, MPCG-Net improves the average accuracy in the -20 to 0 dB low-SNR range by 6.90, 6.45, and 1.66 percentage points on RadioML2016.10A, RadioML2016.10B, and RML22, respectively, while maintaining a lightweight model scale.