MPITE: Multidimensional Performance Evaluator for Interpretable and Traceable Network Performance Evaluation

Xun Yuan

doi:10.1109/TON.2025.3562348

MPITE: Multidimensional Performance Evaluator for Interpretable and Traceable Network Performance Evaluation

0

PRE

AI

DOI：10.1109/TON.2025.3562348

Original

Original request for help

Save

Abstract

En 中文

With the advancements in six-generation (6G) communication technology, there is a growing need for comprehensive and interpretable network performance evaluation for network optimization. Traditional evaluation methods often overlook uncertainties and are limited to a single time scale or performance dimension, while the recent machine learning-based method lacks interpretability. To address this issue, we propose a multidimensional performance evaluator for interpretable and traceable network performance evaluation (MPITE). MPITE, constructed with a three-layer evaluation model incorporating physical, logical, and causal topology structures, reflects the causal relationship of communication system configurations, the changing network states, and performance metrics. We introduce a multidimensional performance index that considers value, time, and certainty dimensions to evaluate network performance comprehensively. We propose interpretable Bayesian theory-based network inference algorithms to derive network certainty for interpretable network performance evaluation. Then, we intelligently derive optimal network configuration parameters through reverse inferencing for network tracing. Experimental results demonstrate the advantage, interpretability, and traceability of MPITE.

Keywords:

Evaluable network

multidimensional performance index

performance evaluation

Bayesian network

MPITE: Multidimensional Performance Evaluator for Interpretable and Traceable Network Performance Evaluation

Abstract

Journal

Organization

Cited Papers