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网络实验的MDE和LLM协同作用:在Beaulieu-Xie色和 κ-μ共同通道干扰环境中对无线系统性能进行案例分析,并将多样性与多样性结合起来

Dragana Krstic¹, Suad Suljovic², Goran Djordjevic²

¹Faculty of Electronic Engineering, University of Nis, 18000 Nis, Serbia.

Sensors (Basel, Switzerland)

|May 25, 2024

概括

本研究分析了使用先进的色和干扰模型的无线通信系统性能. 该研究提供了适用于各种无线通道条件的灵活性能评估工具.

科学领域:

无线通信系统工程无线通信系统工程
信号处理信号处理
统计建模统计建模

背景情况:

准确的通道建模对于无线通信系统设计至关重要.
现有的模型可能无法完全捕捉色和共同通道干扰 (CCI) 的复杂性.
波利欧-谢 (BX) 和 κ-μ 分布在模拟这些现象时提供了更大的灵活性.

研究的目的:

分析多行业选择的性能,将 (SC) 多样性接收器在色和CCI下结合起来.
使用新型分布模型来导出关键绩效指标 (KPIs) 的数学表达式.
为自动化网络实验提供一个框架,将LLMs和MDE集成到性能评估中.

主要方法:

模拟使用Beaulieu-Xie (BX) 分布的色,容纳视线 (LOS) 和非视线 (NLOS) 组件.
模拟使用 κ-μ 分布的共同通道干扰 (CCI).
导出输出信号对共同通道干扰比率 (SIR) 的概率密度函数 (PDF) 和累积分布函数 (CDF),然后计算中断概率 (Pout),通道容量 (CC),动量生成函数 (MGF),平均比特错误概率 (ABEP),水平交叉率 (LCR) 和平均色持续时间 (AFD).

主要成果:

为SIR的PDF和CDF衍生表达式,允许计算各种性能指标.
数值结果表明色/CCI参数和接收分支数量对系统性能的影响.
由此得出的公式可以通过参数调整来适应先前已知的色和CCI分布.

结论:

该研究为在各种道条件下评估无线系统性能提供了通用框架.
拟议的方法提高了性能分析对广泛的色和CCI场景的适用性.
与LLM和MDE的集成有助于自动化网络实验和性能验证.

关键词:

波利尤 - 希正在色.第一个阶级的表现.大型语言模型模型驱动工程是指模型驱动的工程.第二阶段的绩效表现.选择组合组合的选择.κ-μ 共同通道干扰

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