论文标题
改进的基于自适应融合网络的自动调制分类方案
An Improved Automatic Modulation Classification Scheme Based on Adaptive Fusion Network
论文作者
论文摘要
由于样本不平衡引起的过度拟合问题，在噪声方案中，仍然存在改善数据驱动的自动调制分类（AMC）性能的空间。通过完全考虑信号特性，在这项工作中提出了基于自适应融合网络（AFNET）的AMC方案。 AFNET可以智能提取和汇总的相期和正交（I/Q）信号的多尺度空间特征，从而提高特征表示能力。此外，提出了一种新颖的信心加权损失功能来解决不平衡问题，并通过两阶段的学习方案实施。通过两阶段的学习，AFNET可以专注于具有更有效信息的高信任样本并提取有效的表示，以提高整体分类效果。在模拟中，所提出的方案在广泛的SNR上达到62.66％的平均精度，这表现优于其他AMC模型。进一步研究了损失函数对分类精度的影响。
Due to the over-fitting problem caused by imbalance samples, there is still room to improve the performance of data-driven automatic modulation classification (AMC) in noisy scenarios. By fully considering the signal characteristics, an AMC scheme based on adaptive fusion network (AFNet) is proposed in this work. The AFNet can extract and aggregate multi-scale spatial features of in-phase and quadrature (I/Q) signals intelligently, thus improving the feature representation capability. Moreover, a novel confidence weighted loss function is proposed to address the imbalance issue and it is implemented by a two-stage learning scheme.Through the two-stage learning, AFNet can focus on high-confidence samples with more valid information and extract effective representations, so as to improve the overall classification performance. In the simulations, the proposed scheme reaches an average accuracy of 62.66% on a wide range of SNRs, which outperforms other AMC models. The effects of the loss function on classification accuracy are further studied.
