Fusing Time-Domain and Constellation Views: A Multimodal MAE for Wireless Signals (Student Abstract)

This paper introduces a multimodal masked autoencoder (MMAE) that jointly denoise and classifies signals by fusing time-domain IQ sequences and constellation diagrams within a cross-attentive transformer. The approach treats noise as a learnable modality to enhance robustness. A dynamic masking curriculum combines with domain-adversarial training and a hybrid loss function to promote domain-invariant features. Experimentation on RadioML 2018.01A and RadioML22 demonstrates superior accuracy across different SNR conditions while using substantially less labeled data than state-of-the-art approaches.