发表机构
Shaanxi Key Laboratory of Information Communication Network and Security, Xi’an University of Posts and Telecommunications; School of Engineering, University of Warwick（西安邮电大学; 华威大学工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对单天线流体天线系统（FAS）中断概率评估，提出一种面向OP的极值分布信道拟合策略，采用组合极值分布（CEVD）和修正均方误差准则，显著提升超低中断区域的评估精度。
AI 中文摘要
使用极值分布（EVDs）对单天线流体天线系统（FAS）中的信道进行建模，为FAS性能评估提供了一个精确且易处理的框架。当FAS信道拟合的目标是中断概率（OP）评估时，对低概率左尾区域的精确刻画变得至关重要，而现有的强调全局拟合精度的拟合策略可能无法捕捉到精确OP评估所需的关键尾部行为。在本文中，我们提出了一种面向OP的信道拟合策略，该策略采用对左尾敏感的目标分布和拟合准则。具体而言，引入了一种组合极值分布（CEVD）作为目标分布，其中使用广义帕累托分布（GPD）来刻画左尾，使用广义极值（GEV）分布来建模全局行为。此外，我们开发了一种修正均方误差（MMSE）准则，该准则利用对数域误差来增强对左尾差异的敏感性。同时，通过在累积分布函数的对数上进行均匀离散化来构造评估点，确保在所有概率尺度上均匀采样。这减轻了尾部误差在整体MMSE中代表性不足的问题，而由于尾部样本的稀疏性，仅靠误差放大无法有效解决这一问题。仿真结果表明，所提出的拟合策略在超低OP区域显著提高了OP评估精度。
英文摘要
Modeling the channel in a single-antenna fluid antenna system (FAS) using extreme value distributions (EVDs) provides an accurate and tractable framework for FAS performance evaluation. When the objective of FAS channel fitting is outage probability (OP) evaluation, accurate characterization of the low-probability left-tail region becomes crucial, while existing fitting strategies that emphasize global fitting accuracy may fail to capture the critical tail behavior required for precise OP evaluation. In this paper, we propose an OP-oriented channel fitting strategy with a left-tail-sensitive target distribution and fitting criterion. Specifically, a combined EVD (CEVD) is introduced as the target distribution, where a generalized Pareto distribution (GPD) is employed to characterize the left tail and a generalized extreme value (GEV) distribution is used to model the global behavior. Furthermore, a modified mean-square-error (MMSE) criterion is developed, which employs logarithmic-domain errors to enhance sensitivity to left-tail discrepancies. Meanwhile, the evaluation points are constructed via uniform discretization on the logarithm of the cumulative distribution function, ensuring uniform sampling across all probability scales. This mitigates the under-representation of tail errors in the overall MMSE, which cannot be effectively addressed by error amplification alone due to the sparsity of tail samples. Simulation results demonstrate that the proposed fitting strategy significantly improves the OP evaluation accuracy in the ultra-low-OP regime.
