发表机构
National and Kapodistrian University of Athens (NKUA); Hunan University; Nanjing University of Aeronautics and Astronautics; King Fahd University of Petroleum & Minerals (KFUPM)（雅典国立卡波迪斯特里安大学; 湖南大学; 南京航空航天大学; 法赫德国王石油与矿产大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文推导了逆伽马随机变量之和的精确闭式PDF和CDF，并在无人机辅助网络和高速铁路通信中验证了其准确性，为无线通信性能分析提供了稳定易用的工具。
AI 中文摘要
研究随机变量之和的统计特性是无线通信系统的基础，因为它可用于准确估计系统性能。本文推导了逆伽马随机变量之和的概率密度函数和累积分布函数的精确闭式表达式。逆伽马分布特别适用于性能分析，因为它提供了阴影衰落/局部平均功率变化的易处理且准确的模型。与以往关于阴影衰落随机变量之和的研究不同，所得公式易于评估且数值稳定。其效用通过两个实际场景得到验证：无人机辅助网络和高速铁路通信。在所有考虑的场景中，解析结果与蒙特卡洛模拟结果高度一致，而渐近近似紧密跟踪精确结果。
英文摘要
Investigating the statistics of the sum of random variables (RVs) is fundamental in wireless communication systems, as it can be used to accurately estimate their performance. In this paper, exact closed-form expressions are derived for the probability density function (PDF) and cumulative distribution function (CDF) of the sum of inverse-gamma (IG) RVs. The IG distribution is especially suitable for performance analysis purposes because it provides a tractable yet accurate model of shadowing/local mean power variations. Unlike prior studies on sums of shadowing RVs, the resulting formulas are simple to evaluate and numerically stable. Their utility is demonstrated in two practical settings: unmanned aerial vehicle (UAV)-assisted networks and high-speed railway communications. Across all considered scenarios, the analytical results are in excellent agreement with the Monte Carlo simulation results, while the asymptotic approximations closely track the exact results
Journal refIEEE Transactions on Vehicular Technology. 2026
