Cross-Validated Cross-Channel Self-Attention and Denoising for Automatic Modulation Classification
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. A deep learning AMC model was proposed, incorporating a cross-channel self-attention block to capture dependencies between in-phase and quadrature components, along with dual-path deep residual shrinkage denoising blocks to suppress noise. Experiments using the RML2018.01a dataset e...
| Authors | Prakash Suman, Yanzhen Qu |
|---|---|
| Primary category | cs.LG |
| Published | 11 April 2026 |
| Last updated at source | 11 April 2026 |
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