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.

中文翻译：

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Wi-Fi 中基于前导码的数据包检测：一种深度学习方法

基于 IEEE 802.11 标准系列的 Wi-Fi 系统在非授权频段运行，是最流行的无线接口，使用先听后话 (LBT) 方法进行信道访问。大多数基于 LBT 的系统的显着特点是发射器使用数据之前的前导码，以允许接收器获取初始信号检测和同步。在模数转换之后应用在传入离散时间复基带样本上的接收器的第一个数字处理步骤是数据包检测步骤，即检测到达传入流中的每个帧的初始样本. 由于前导通常包含具有良好相关性的重复训练符号，传统的数字接收器应用基于相关的方法进行数据包检测。随着最近对用于物理层信号处理的基于数据的深度学习 (DL) 方法的兴趣，在本文中，我们用基于 DL 的 Wi-Fi 数据包检测方法挑战了传统方法。使用一维卷积神经网络 (1D-CNN)，我们提供了详细的复杂性与性能分析以及传统和基于 DL 的 Wi-Fi 数据包检测方法之间的比较。