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

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.