论文标题
使用深神经网络的RF信号转换和分类
RF Signal Transformation and Classification using Deep Neural Networks
论文作者
论文摘要
为计算机视觉和自然语言处理任务设计的深神经网络（DNN）不能直接应用于射频（RF）数据集。为了应对这一挑战，我们建议通过引入卷积变换技术将原始RF数据转换为适合现成DNN的数据类型。此外，我们提出了一个简单的5层卷积神经网络体系结构（CORV-5），可以使用RAW RF I/Q数据运行，而无需进行任何转换。此外，我们提出了一个称为RF1024的RF数据集，以促进未来的RF研究。 RF1024由8个不同的RF调制类组成，每个类具有1000/200培训/测试样本。 RF1024数据集的每个样本都包含1024个复合物I/Q值。最后，实验是在RadiOML2016和RF1024数据集上进行的，以证明改进的分类性能。
Deep neural networks (DNNs) designed for computer vision and natural language processing tasks cannot be directly applied to the radio frequency (RF) datasets. To address this challenge, we propose to convert the raw RF data to data types that are suitable for off-the-shelf DNNs by introducing a convolutional transform technique. In addition, we propose a simple 5-layer convolutional neural network architecture (CONV-5) that can operate with raw RF I/Q data without any transformation. Further, we put forward an RF dataset, referred to as RF1024, to facilitate future RF research. RF1024 consists of 8 different RF modulation classes with each class having 1000/200 training/test samples. Each sample of the RF1024 dataset contains 1024 complex I/Q values. Lastly, the experiments are performed on the RadioML2016 and RF1024 datasets to demonstrate the improved classification performance.
