O-RAN testing is becoming increasingly difficult with the exponentially growing number of performance measurements as the system grows more complex, with additional units, interfaces, applications, and possible implementations and configurations. To simplify the testing procedure and improve system design for O-RAN systems, it is important to identify the dependencies among various performance measurements, which are inherently time-series and can be modeled as realizations of random processes. While information theory can be utilized as a principled foundation for mapping these dependencies, the robust estimation of such measures for random processes from real-world data remains challenging. This paper introduces AMIF-MDS, which employs aggregate mutual Information in frequency (AMIF), a practical proxy for directed information (DI), to quantify similarity and visualize inter-series dependencies with multidimensional scaling (MDS). The proposed quantile-based AMIF estimator is applied to O-RAN time-series testing data to identify dependencies among various performance measures so that we can focus on a set of ``core'' performance measures. Applying density-based spatial clustering of applications with noise (DBSCAN) to the MDS embedding groups mutually informative metrics, organically reveals the link-adaptation indicators among other clusters, and yields a ``core'' performance measure set for future learning-driven O-RAN testing.

翻译：随着系统日益复杂，单元、接口、应用以及可能的实现与配置不断增加，O-RAN测试中性能测量指标数量呈指数级增长，使得测试工作变得愈发困难。为简化O-RAN系统的测试流程并改进系统设计，识别各类性能测量指标之间的依赖关系至关重要；这些指标本质上是时间序列数据，可建模为随机过程的实现。虽然信息论可作为刻画这些依赖关系的理论基础，但基于实际数据对随机过程的相关度量进行稳健估计仍具挑战性。本文提出AMIF-MDS方法，该方法采用频域聚合互信息（一种针对有向信息的实用替代度量）来量化相似性，并利用多维尺度分析实现序列间依赖关系的可视化。所提出的基于分位数的AMIF估计器被应用于O-RAN时间序列测试数据，以识别不同性能指标间的依赖关系，从而帮助聚焦于一组“核心”性能指标。通过对MDS嵌入结果应用基于密度的噪声应用空间聚类算法，可将互信息丰富的指标进行分组，自然地揭示出链路自适应指标与其他簇的关联，并为未来基于学习的O-RAN测试生成一套“核心”性能指标集合。