Non-cooperative signal recognition: spectrum augmentation and optimization method for modulation style analysis and contrastive clustering

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Abstract In non-cooperative scenarios, signal modulation style analysis has received considerable interest as a method for avoiding malicious attacks in modern cognitive communication systems. However, existing signal clustering methods often overlook the potential of the spectrum and fail to address the issue of local optima commonly encountered in the field of signal clustering. In this paper, we introduce a novel approach to accurately cluster modulation styles, termed Spectrum Augmentation for Contrastive Clustering (SACC). SACC proposes two main components, namely, spectrum augmentation and self-labeling optimization, building upon the foundation of contrastive clustering. Specifically, spectrum augmentation (SA) is employed in the first stage to facilitate effective deep semantic feature extraction, leveraging contrastive learning. SA is introduced as a signal spectrum-based data augmentation method, exploiting the temporal and frequency representation capability of signals. In the second stage, a reliable negative self-labeling optimization method is proposed atop deep clustering to address the issue of local optima arising from the lack of label guidance in signal clustering. Extensive experimental results validate the effectiveness of the SACC method, SACC achieving significant performance improvements with a straightforward design. Specifically, SACC achieves an accuracy rate of 80.1% and a normalized mutual information score of 0.826 on two publicly available datasets, demonstrating the superiority of our proposed approach.