Zhuoran Li

Automatic Modulation Classification (AMC) aims to automatically identify modulation types based on the features of received signals, and acts as a vital technique for spectrum sensing, cognitive radio and electronic countermeasures. However, existing methods generally overlook the issue that modulation signals with similar characteristics are prone to confusion. In particular, under strong interference conditions, feature distributions become blurred and class boundaries tend to overlap, further exacerbating misclassification among modulation types with similar characteristics, thereby limiting improvements in classification accuracy and model robustness. To address this challenge, a modulation classification method based on fuzzy sample feature enhancement is proposed. Specifically, a modulation class entropy constraint and a fuzzy sample feature enhancement constraint are introduced to establish a Multi-scale Fuzzy Sample Feature Enhancement Framework (MTFSFEF). Through fuzzy sample selection and feature representation refinement, the proposed framework effectively mitigates feature overlap among fuzzy samples and enhances inter-class separability and discriminability. For $\mathrm{SNR}\ge 0\mathrm{dB}$, MTFSFEF delivers superior average classification accuracies across all three benchmark datasets: $92.82\%$ on RML2016.10a, over $93.43\%$ on RML2016.10b, and exceeding $92.78\%$ on RML2018.01a, outperforming existing methods by up to $2.68\%$, $2.89\%$, and $2.73\%$, respectively.