Manifold-Regularized Latent Diffusion Model for Partition-Robust Open Set Wireless Signal Recognition

Wireless Signal Recognition (WSR) leverages deep learning to perform blind estimation of signal attributes, serving as the core technology for spectrum monitoring and wireless security. Real-world spectrum environments are open and dynamic, which requires models to accurately classify known signals while reliably detecting unknown types. This defines the open set WSR problem. Despite its importance, robust open set WSR remains underexplored due to a fundamental barrier: partition sensitivity, where model performance and rankings fluctuate across different data partitions, undermining evaluation consistency and deployment reliability. This challenge is particularly acute for wireless signals: channel-induced perturbations cause substantial intra-class feature variation, while inherent structural similarities among signal types render inter-class boundaries fragile. To address this, we formalize the problem as Partition-Robust Risk Minimization (PRRM), which minimizes a surrogate risk that jointly optimizes expected performance and suppresses risk variance. The variance decomposes into sample-level variance and class-composition variance. Guided by PRRM, we introduce DualDiff, which employs manifold-regularized latent diffusion to construct a smooth feature manifold that reduces sensitivity to sample-level perturbations, and geometry-aware fine-tuning to stabilize class boundaries against subset composition changes. Extensive experiments demonstrate that DualDiff achieves strong open set recognition performance while maintaining consistent rankings across diverse data partitions.