A Physics-Aware Collaborative Framework with Prototype Consistency for Noisy Labels Signal Modulation Classification

Signal Modulation Classification (SMC) is a fundamental technique in wireless communications. However, the prevalence of label noise in practical scenarios severely constrains the advancement of SMC technology. Existing SMC methods heavily rely on high-quality labeled data and often underutilize the inherent physical prior knowledge of signals. To address these issues, this article proposes a Physics-Aware Collaborative Framework with Prototype Consistency (PhyCo-PC), designed for noisy label environments and operating without requiring reliable labels. Firstly, the framework leverages co-teaching for noise identification and incorporates a collaborative consensus-guided module for prototype learning and pseudo-label generation. Secondly, it constructs physics-guided downstream decision module that fuses deep learning features with instantaneous physical signal characteristics to enhance decision robustness. Thirdly, a Domain Knowledge-guided Adaptive Sample Selection (DKASS) strategy is introduced. DKASS parameterizes the selection rate scheduling function, incorporates domain knowledge to constrain the search space, and utilizes automated search for optimization. This enables the model to adaptively determine the optimal training strategy for varying noise environments. Finally, experimental results demonstrate that PhyCo-PC significantly improves SMC classification performance under complex label noise scenarios on the RML2016.10a/04c datasets, exhibiting excellent robustness and significant advantages.