论文标题
IEEE 802.11AH中的基于深度学习的数据包检测和载体频率偏移估计
Deep Learning Based Packet Detection and Carrier Frequency Offset Estimation in IEEE 802.11ah
论文作者
论文摘要
基于IEEE 802.11标准的Wi-Fi系统是最受欢迎的无线接口，在通信访问频道访问之前使用Talling（LBT）方法。大多数基于LBT的系统的独特特征是发射器使用在数据之前的前置序列，以允许接收器执行数据包检测和载波频率偏移量（CFO）估计。序言通常包含具有良好相关属性的训练符号的重复，而常规数字接收器将基于相关的方法用于数据包检测和CFO估计。但是，近年来，基于数据的机器学习方法正在破坏物理层研究。特别是在基于深度学习的领域（DL）的渠道估计中提出了有希望的结果。在本文中，我们使用常规方法和基于DL的方法对数据包检测和CFO估计进行了性能和复杂性分析。该研究的目的是在哪个条件下研究基于DL的方法方法的性能，甚至超过常规方法，但在哪些条件下，其性能较低。我们的调查专注于新兴的IEEE 802.11AH标准，同时使用了基于标准的模拟环境，也使用了基于软件定义的无线电的现实世界测试床。
Wi-Fi systems based on the IEEE 802.11 standards are the most popular wireless interfaces that use Listen Before Talk (LBT) method for channel access. The distinctive feature of a majority of LBT-based systems is that the transmitters use preambles that precede the data to allow the receivers to perform packet detection and carrier frequency offset (CFO) estimation. Preambles usually contain repetitions of training symbols with good correlation properties, while conventional digital receivers apply correlation-based methods for both packet detection and CFO estimation. However, in recent years, data-based machine learning methods are disrupting physical layer research. Promising results have been presented, in particular, in the domain of deep learning (DL)-based channel estimation. In this paper, we present a performance and complexity analysis of packet detection and CFO estimation using both the conventional and the DL-based approaches. The goal of the study is to investigate under which conditions the performance of the DL-based methods approach or even surpass the conventional methods, but also, under which conditions their performance is inferior. Focusing on the emerging IEEE 802.11ah standard, our investigation uses both the standard-based simulated environment, and a real-world testbed based on Software Defined Radios.
