Towards Cross-Channel Scenarios: Fusion Semi-Supervised Adversarial Domain Adaptation Modulation Classification Network

Automatic Modulation Classification (AMC) using deep learning techniques has become a prominent area of research, showcasing considerable practical applications. However, the present AMC deep learning network, trained on a specific channel model, performs poorly in a new channel scenario. To deal with this, an adversarial semi-supervised domain adaptation method is proposed. Specifically, classification accuracy, cluster sensitivity, and distribution distance are optimized together, utilizing a three-stage iterative training approach. As a result, the proposed model has achieved robust performance with a limited amount of labeled data when shifting to a new channel.