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arXiv 2104.11516eess.SP
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基于极值理论的超可靠通信非平稳无线信道建模方法

Non-Stationary Wireless Channel Modeling Approach Based on Extreme Value Theory for Ultra-Reliable Communications

Niloofar Mehrnia, Sinem Coleri

AI总结:

本文提出一种基于极值理论(EVT)的非平稳无线信道建模方法,通过分组拟合广义帕累托分布和确定时变阈值,在超可靠通信场景下显著优于平稳假设方法。

摘要:

一种能够表征极端事件统计特性的适当信道建模方法,是设计在超可靠运行机制下系统的关键。超可靠性的严格约束对应于在毫秒级的可接受延迟范围内,$10^{-9}-10^{-5}$ 之间的分组错误率(PER)。极值理论(EVT)是一个用于对信道数据中极端事件的统计行为进行建模的稳健框架。在本文中,我们提出了一种基于 EVT 的方法,用于对非平稳无线信道的极端事件进行建模,以适用于超可靠运行机制。该方法包括将信道数据序列按照引起非平稳性的环境因素划分为多个组,并将每组中接收功率的下尾分布拟合到广义帕累托分布(GPD)。所提方法还包括最优地确定时变阈值(尾部统计量作为时间的函数基于该阈值导出),并评估导出的 Pareto 模型的有效性。最后,所提方法选择具有最小复杂度的最佳模型来表示非平稳信道数据序列的时间变化行为。基于在 Fiat Linea 发动机舱内不同发动机振动和驾驶场景下收集的数据,我们证明了所提方法能够为非平稳数据的极端情况提供最佳拟合,在表征极端事件方面显著优于基于平稳信道假设的信道建模方法。

英文摘要:

A proper channel modeling methodology that characterizes the statistics of extreme events is key in the design of a system at an ultra-reliable regime of operation. The strict constraint of ultra-reliability corresponds to the packet error rate (PER) in the range of $10^{-9}-10^{-5}$ within the acceptable latency on the order of milliseconds. Extreme value theory (EVT) is a robust framework for modeling the statistical behavior of extreme events in the channel data. In this paper, we propose a methodology based on EVT to model the extreme events of a non-stationary wireless channel for the ultra-reliable regime of operation. This methodology includes techniques for splitting the channel data sequence into multiple groups concerning the environmental factors causing non-stationarity, and fitting the lower tail distribution of the received power in each group to the generalized Pareto distribution (GPD). The proposed approach also consists of optimally determining the time-varying threshold over which the tail statistics are derived as a function of time, and assessing the validity of the derived Pareto model. Finally, the proposed approach chooses the best model with minimum complexity that represents the time variation behavior of the non-stationary channel data sequence. Based on the data collected within the engine compartment of Fiat Linea under various engine vibrations and driving scenarios, we demonstrate the capability of the proposed methodology in providing the best fit to the extremes of the non-stationary data, significantly outperforming the channel modeling approach in characterizing the extreme events with the stationary channel assumption.

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。

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