Open Set Domain Adaptation for Automatic Modulation Classification in Dynamic Communication Environments

Automatic modulation classification (AMC) is gaining greater significance in both military and civilian contexts. However, the diversity and dynamics of actual wireless communication environments can cause shifts in signal data distribution, posing risks of encountering unfamiliar modulations and negatively impacting recognition performance. To tackle these challenges, we propose the Open set domain adaptation for AMC (OSDA-AMC) algorithm. The approach utilizes a partial iterative separation technique, consisting of a pre-processing unit, a deep feature extractor, and meticulously crafted unknown, known, and domain classifiers. OSDA-AMC innovatively introduces an unknown class to the source classifier, facilitating the differentiation between known and unknown class features. Through multiple binary classifiers, the algorithm estimates the similarity between target data and each source class, differentiating unknown and known class data. Iteratively, it separates unknown signals from the target domain and labels them as unknown class. Simultaneously, the known class domain adaptation unit utilizes classifiers and domain discriminator for adversarial domain adaptation within the known class, ensuring similar feature distribution across the two domains. The proposed OSDA-AMC method can enhance the adaptability of AMC for recognizing unknown signals in dynamic channels in real-world environments. The experimental results demonstrate that the algorithm performs better in dynamic communication environments. By utilizing unknown class sample information effectively, we improve the accuracy of recognition and overall robustness.