【摘要】 自动调制分类（AMC）在无线通信中既具挑战性又起到至关重要的作用。尽管基于深度学习的方法在信号处理中得到广泛应用，但它们面临着信号分布变化的挑战，特别是在各种信道条件下。本文介绍了一种基于迁移学习的对抗迁移框架，名为频谱学习对抗网络（FLANs），用于跨场景信号分类。该方法利用频谱稳定性，通过引入频率适应（FA）技术，将目标信道信息融入源域信号。为解决信道中的不可预测干扰，采用拟合信道适应（FCA）模块，以减少因信道环境变化引起的源域和目标域之间的差异。实验结果表明，在高信噪比的跨场景真实采集数据集CSRC2023上，FLANs优于现有最先进迁移方法，其分类准确率最高值提高了5.2个百分点。更多还原

【Abstract】 Automatic modulation classification(AMC) serves a challenging yet crucial role in wireless communications. Despite deep learning-based approaches being widely used in signal processing, they are challenged by signal distribution variations, especially in various channel conditions. In this paper, we introduce an adversarial transfer framework named frequency-learning adversarial networks(FLANs) based on transfer learning for cross-scenario signal classification. This method uses the stability in the frequency spectrum by introducing a frequency adaptation(FA) technique to incorporate target channel information into source-domain signals. To address the unpredictable interference in the channel, a fitting channel adaptation(FCA) module is used to reduce the difference between the source and target domains caused by variations in the channel environment. Experimental results illustrate that FLANs outperforms state-of-the-art transfer approaches, demonstrating an improved top-1 classification accuracy by about 5.2 percentage points in high signal-to-noise ratio(SNR) scenes on a cross-scenario real collected dataset CSRC2023.更多还原