Automatic Modulation Recognition Across SNR Variability Via Domain Adversary

Automatic Modulation Recognition (AMR) is crucial for optimizing communication systems, facilitating effective spectrum management and robust signal processing. Traditional AMR techniques, leveraging Machine Learning and Deep Learning algorithms, perform well under controlled conditions but struggle with domain variability, particularly changes in Signal-to-Noise Ratio (SNR). SNR variability, common in real-world environments due to mobility and interference, degrades classification accuracy and system reliability. In this regard, we propose a novel AMR framework leveraging Domain-Adversarial Neural Networks to address SNR variability. Our approach employs domain adversarial learning techniques to align feature distributions across different SNR levels, mitigating domain shifts and enhancing modulation recognition robustness. Extensive experiments demonstrate significant improvements in classification accuracy compared to existing techniques, highlighting the potential of domain adversarial methods in overcoming domain discrepancies in AMR.