IEEE 802.11ah中基于深度学习的分组检测和载波频偏估计
Deep Learning Based Packet Detection and Carrier Frequency Offset Estimation in IEEE 802.11ah
摘要：
基于IEEE 802.11标准的Wi-Fi系统是使用先听后说（LBT）方法进行信道接入的最流行的无线接口。大多数基于LBT的系统的显著特征是发射机使用在数据之前的伪码，以允许接收机执行分组检测和载波频率偏移（CFO）估计。前导码通常包含具有良好相关特性的训练符号的重复，而传统的数字接收机将基于相关的方法应用于分组检测和CFO估计两者。然而，近年来，基于数据的机器学习方法正在颠覆物理层研究。特别是在基于深度学习（DL）的信道估计领域中，已经提出了有希望的结果。在本文中，我们提出了一个性能和复杂性分析的数据包检测和CFO估计使用传统的和基于DL的方法。该研究的目标是调查在哪些条件下，基于DL的方法的性能接近甚至超过传统的方法，但也，在哪些条件下，他们的性能较差。针对新兴的IEEE 802.11ah标准，我们的研究使用了基于标准的模拟环境和基于软件定义无线电的真实测试平台。
英文摘要：
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.
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。
