Compositional Zero-Shot Recognition based on Tangent Space Disentanglement for Composite Modulation Signals

Yurui Zhao, Xiang Wang, Zhitao Huang, Baoguo Li

Automatic composite modulation recognition (ACMR) is critical for integrated sensing and communication (ISAC) systems, while conventional approaches face significant challenges due to the semantic coupling between inner-layer and outer-layer modulations in composite modulation (CM), degraded performance under joint hardware and channel imperfections, and limited capability to handle unknown modulation schemes. To this end, we design a disentangled semantic space and propose zero-shot learning framework. Within this framework, a logarithmic projection first linearizes the multiplicative coupling between modulation layers and a learnable geometric transformation is used for layer-wise semantic features. We instantiate the framework as the Tangent Space Disentanglement Network (TSDN). TSDN integrates logarithmic mapping, a spatial transformer network for learning the geometric transformation, and a multi-objective loss function that balances discrimination with cross-domain generalization. Comprehensive experiments demonstrate that TSDN achieves over 93\% zero-shot recognition accuracy, outperforms unified-semantic and multi-task baselines by significant margins, and maintains robust performance under combined channel fading and hardware imperfections down to 4 dB SNR.