AI RESEARCH
Cross-Validated Cross-Channel Self-Attention and Denoising for Automatic Modulation Classification
arXiv CS.LG
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ArXi:2604.10054v1 Announce Type: new This study addresses a key limitation in deep learning Automatic Modulation Classification (AMC) models, which perform well at high signal-to-noise ratios (SNRs) but degrade under noisy conditions due to conventional feature extraction suppressing both discriminative structure and interference. The goal was to develop a feature-preserving denoising method that mitigates the loss of modulation class separation.