论文标题
基于极端价值理论的无线通道建模
Wireless Channel Modeling Based on Extreme Value Theory for Ultra-Reliable Communications
论文作者
论文摘要
超可靠通信系统设计中的一个关键构建块是一个无线通道模型，可捕获由于褪色大量褪色而发生的罕见事件的统计数据。在本文中，我们提出了一种基于极端价值理论（EVT）的新方法，以统计地对无线通道中极端事件的行为进行统计模型，以进行超可靠的通信。该方法包括拟合接收功率的较低尾巴分布到广义帕累托分布（GPD）的技术，从而确定了尾部统计数据得出的最佳阈值，从而确定了使用GPD估算尾巴统计量所需的最佳停止条件，并最终评估了派生的帕特托模型的有效性。基于在各种发动机振动和驾驶场景下，菲亚特线发动机室内收集的数据，我们证明了所提出的方法为收集的数据提供了最佳拟合，从而极大地胜过基于传统的外推方法。此外，在提出的方法中使用EVT的用法减少了估计尾部统计数据的所需样本数量约7美元\ times 10^5 $。
A key building block in the design of ultra-reliable communication systems is a wireless channel model that captures the statistics of rare events occurring due to significant fading. In this paper, we propose a novel methodology based on extreme value theory (EVT) to statistically model the behavior of extreme events in a wireless channel for ultra-reliable communication. This methodology includes techniques for fitting the lower tail distribution of the received power to the generalized Pareto distribution (GPD), determining the optimum threshold over which the tail statistics are derived, ascertaining the optimum stopping condition on the number of samples required to estimate the tail statistics by using GPD, and finally, assessing the validity of the derived Pareto model. Based on the data collected within the engine compartment of Fiat Linea under various engine vibrations and driving scenarios, we demonstrate that the proposed methodology provides the best fit to the collected data, significantly outperforming the conventional extrapolation-based methods. Moreover, the usage of the EVT in the proposed method decreases the required number of samples for estimating the tail statistics by about $7 \times 10^5$.
