The dataset HKDD_AMC12 of paper "Towards Next-Generation Signal Intelligence: A Hybrid Knowledge and Data-Driven Deep Learning Framework for Radio Signal Classification".

Here we publish the dataset HKDD_AMC12 used in the paper "Towards Next-Generation Signal Intelligence: A Hybrid Knowledge and Data-Driven Deep Learning Framework for Radio Signal Classification". Automatic modulation classification (AMC) can generally be divided into knowledge-based methods and data-driven methods. In this paper, we explore combining the knowledgebased method and data-driven technology to take full advantage of both and propose a hybrid knowledge and data-driven deep learning framework (HKDD) for AMC. To make the handcrafted features more discriminative, various traditional features are adopted, including instantaneous features, statistical features, and spectral features. In the HKDD framework, a feature fusion mechanism is proposed to integrate the features learned from the original signal with those processed by a fully connected network from the handcrafted features. Besides, an attention mechanism is implemented on the fused features to neglect immature features and highlight important features. To evaluate the performance of the proposed method, we construct two modulation classification datasets containing both traditional features and raw IQ data. Simulation results show that our proposed method has significant performance gain in both adequate-sample classification scenario and few-shot classification scenario.

本文公开了论文《面向下一代信号智能：一种用于无线电信号分类的知识与数据驱动混合深度学习框架》中所使用的HKDD_AMC12数据集。自动调制分类（Automatic Modulation Classification, AMC）通常可划分为基于知识的方法与数据驱动方法两大类。本文旨在将基于知识的方法与数据驱动技术相结合，以充分发挥二者的优势，并提出了一种面向AMC的知识与数据驱动混合深度学习框架（HKDD）。为使手工特征（handcrafted features）具备更强的判别性，本文采用了多种传统特征，涵盖瞬时特征、统计特征与频谱特征。在HKDD框架中，本文提出了一种特征融合机制，将从原始信号中学习得到的特征，与基于手工特征通过全连接网络（fully connected network）处理后得到的特征进行融合。此外，本文还在融合特征上引入了注意力机制（attention mechanism），以忽略非有效特征并突出关键特征。为评估所提方法的性能，本文构建了两个同时涵盖传统特征与原始IQ数据的调制分类数据集。仿真结果表明，本文所提方法在充足样本分类场景与少样本分类场景下均实现了显著的性能增益。