使用监督与无监督学习结合频谱图检测5G信号干扰器
Detecting 5G Signal Jammers Using Spectrograms with Supervised and Unsupervised Learning
AI总结：
针对5G网络受干扰攻击可能引发严重损害的问题，本文提出基于5G信号IQ样本生成的频谱图，对比了基于CNN的监督模型与基于CAE的无监督模型的干扰检测准确率与计算复杂度。
摘要：
蜂窝网络是干扰攻击的潜在目标，这类攻击会破坏无线通信。由于第五代（5G）蜂窝网络支持自动驾驶或智能制造等任务关键型应用，由此引发的故障可能造成严重损害。本文提出通过对频谱图进行在线分类来检测宽带干扰器。这些频谱图由5G无线电信号的同相和正交（IQ）样本流计算得出。我们通过实验获取这些信号，并阐述了如何设计合适的数据集用于训练。基于该数据，我们比较了两种分类方法：基于基础卷积神经网络（CNN）构建的监督学习模型，以及基于卷积自编码器（CAE）的无监督学习模型。在比较了这些模型的结构后，我们从准确率和计算复杂度两方面评估了它们的性能。
英文摘要：
Cellular networks are potential targets of jamming attacks to disrupt wireless communications. Since the fifth generation (5G) of cellular networks enables mission-critical applications, such as autonomous driving or smart manufacturing, the resulting malfunctions can cause serious damage. This paper proposes to detect broadband jammers by an online classification of spectrograms. These spectrograms are computed from a stream of in-phase and quadrature (IQ) samples of 5G radio signals. We obtain these signals experimentally and describe how to design a suitable dataset for training. Based on this data, we compare two classification methods: a supervised learning model built on a basic convolutional neural network (CNN) and an unsupervised learning model based on a convolutional autoencoder (CAE). After comparing the structure of these models, their performance is assessed in terms of accuracy and computational complexity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
