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.

中文翻译：

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动态通信环境中自动调制分类的开集域自适应


自动调制分类（AMC）在军事和民用领域都变得越来越重要。然而，实际无线通信环境的多样性和动态性可能会导致信号数据分布的变化，从而带来遇到不熟悉的调制并对识别性能产生负面影响的风险。为了应对这些挑战，我们提出了 AMC 的开放集域自适应（OSDA-AMC）算法。该方法采用部分迭代分离技术，由预处理单元、深度特征提取器和精心设计的未知、已知和领域分类器组成。 OSDA-AMC创新性地将未知类引入到源分类器中，有利于区分已知类特征和未知类特征。通过多个二元分类器，该算法估计目标数据与每个源类之间的相似性，区分未知类和已知类数据。迭代地，它从目标域中分离出未知信号并将其标记为未知类。同时，已知类域适应单元利用分类器和域鉴别器在已知类内进行对抗性域适应，确保两个域之间的相似特征分布。所提出的OSDA-AMC方法可以增强AMC在现实环境中识别动态信道中未知信号的适应性。实验结果表明，该算法在动态通信环境中表现更好。通过有效利用未知类样本信息，我们提高了识别的准确性和整体鲁棒性。