AI 中文总结
针对复合调制信号识别难题，提出基于切线空间解缠的零样本学习框架TSDN，通过对数投影和可学习几何变换处理语义特征，集成对数映射等，实验证明其零样本识别准确率超93%，性能优于基线且在低SNR下稳健。
AI 中文摘要
自动复合调制识别（ACMR）对综合感知与通信（ISAC）系统至关重要。传统方法因复合调制中内外层调制的语义耦合、联合硬件和信道缺陷下性能下降以及处理未知调制方案能力有限而面临重大挑战。为此设计了解缠语义空间并提出零样本学习框架，通过对数投影线性化调制层间乘法耦合，用可学习几何变换处理层语义特征，实例化为切线空间解缠网络（TSDN）。TSDN集成对数映射、学习几何变换的空间变换网络和平衡判别与跨域泛化的多目标损失函数。综合实验表明TSDN零样本识别准确率超93%，大幅优于统一语义和多任务基线，在4dB SNR的联合信道衰落和硬件缺陷下保持稳健性能。
英文摘要
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.
Comments17 pages
