论文标题
Wi-Fi中的基于序言的数据包检测：一种深度学习方法
Preamble-Based Packet Detection in Wi-Fi: A Deep Learning Approach
论文作者
论文摘要
基于IEEE 802.11标准的Wi-Fi系统是在未经执照的频段中运行的标准，是最受欢迎的无线接口，可在Talk（LBT）方法（LBT）方法访问频道访问之前使用侦听。大多数基于LBT的系统的独特特征是，发射机使用在数据之前的前言，允许接收器获取初始信号检测和同步。在模数转换之后，在传入离散时间复杂的基带样本上应用的接收器的第一个数字处理步骤是数据包检测步骤，即检测到传入流中每个帧的初始样品的检测。由于序列通常包含具有良好相关属性的训练符号的重复，因此常规数字接收器采用基于相关的方法进行数据包检测。在本文中，根据对物理层信号处理的最新兴趣（DL）方法，我们以基于DL的Wi-Fi数据包检测来挑战常规方法。使用一维卷积神经网络（1D-CNN），我们提出了详细的复杂性与性能分析以及基于常规和基于DL的Wi-Fi数据包检测方法之间的比较。
Wi-Fi systems based on the family of IEEE 802.11 standards that operate in unlicenced bands are the most popular wireless interfaces that use Listen Before Talk (LBT) methodology for channel access. Distinctive feature of majority of LBT-based systems is that the transmitters use preambles that precede the data to allow the receivers to acquire initial signal detection and synchronization. The first digital processing step at the receiver applied over the incoming discrete-time complex-baseband samples after analog-to-digital conversion is the packet detection step, i.e., the detection of the initial samples of each of the frames arriving within the incoming stream. Since the preambles usually contain repetitions of training symbols with good correlation properties, conventional digital receivers apply correlation-based methods for packet detection. Following the recent interest in data-based deep learning (DL) methods for physical layer signal processing, in this paper, we challenge the conventional methods with DL-based approach for Wi-Fi packet detection. Using one-dimensional Convolutional Neural Networks (1D-CNN), we present a detailed complexity vs performance analysis and comparison between conventional and DL-based Wi-Fi packet detection approaches.
