Most prior works on deep learning-based wireless device classification using radio frequency (RF) data apply off-the-shelf deep neural network (DNN) models, which were matured mainly for domains like vision and language. However, wireless RF data possesses unique characteristics that differentiate it from these other domains. For instance, RF data encompasses intermingled time and frequency features that are dictated by the underlying hardware and protocol configurations. In addition, wireless RF communication signals exhibit cyclostationarity due to repeated patterns (PHY pilots, frame prefixes, etc.) that these signals inherently contain. In this paper, we begin by explaining and showing the unsuitability as well as limitations of existing DNN feature design approaches currently proposed to be used for wireless device classification. We then present novel feature design approaches that exploit the distinct structures of the RF communication signals and the spectrum emissions caused by transmitter hardware impairments to custom-make DNN models suitable for classifying wireless devices using RF signal data. Our proposed DNN feature designs substantially improve classification robustness in terms of scalability, accuracy, signature anti-cloning, and insensitivity to environment perturbations. We end the paper by presenting other feature design strategies that have great potentials for providing further performance improvements of the DNN-based wireless device classification, and discuss the open research challenges related to these proposed strategies.
翻译:使用无线电频率(RF)数据的深学习基础的无线装置分类工作,大部分以前的工作都是利用无线电频率(RF)数据,应用现成的深神经网络(DNN)模型,这些模型主要在视觉和语言等领域成熟;然而,无线RF数据具有独特的特点,将其与其他领域区别开来;例如,RF数据包含由基本硬件和协议配置决定的混合时间和频率特点;此外,无线RF通信信号显示出这些信号所固有的反复模式(PHY试点、框架前缀等)所固有的循环常态性;在本文件中,我们首先解释并展示了目前拟用于无线装置分类的现有DNN特征设计方法的不适宜性和局限性;我们随后介绍了新颖的特性设计方法,利用了RF通信信号的独特结构,以及发射机硬件障碍导致的频谱排放,这些模式适合于利用RF信号数据对无线装置进行分类。我们提议的DNNNP特征特征特征设计在可缩放性、准确性、签名反克隆和对无线结构的高度敏感性方面大幅度改进。