Modeling configuration-performance relation in a mobile network: a data-driven approach

TL;DR

This paper introduces a neural network-based method for modeling the performance of mobile network cells across diverse configurations, enabling more accurate predictions and optimization in multidimensional parameter spaces.

Contribution

It presents a data-driven neural network approach that models configuration-performance relations in mobile networks, outperforming fixed-configuration models and predicting unseen configurations.

Findings

Lower mean absolute error (0.25 vs. 0.45) compared to fixed models

Outperforms single-configuration models in accuracy

Enables performance prediction for unknown configurations

Abstract

Mobile network performance modeling typically assumes either a fixed cell's configuration or only considers a limited number of parameters. This prohibits the exploration of multidimensional, diverse configuration space for, e.g., optimization purposes. This paper presents a method for performance predictions based on a network cell's configuration and network conditions, which utilizes neural network architecture. We evaluate the idea by extensive experiments, with data from more than 50,000 5G cells. The assessment included a comparison of the proposed method against models developed for fixed configuration. Results show that combined configuration-performance modeling outperforms single-configuration models and allows for performance prediction of unknown configurations, i.e., it is not used for model training. A substantially lower mean absolute error was achieved (0.25 vs. 0.45 for fixed-configuration MLP-based models).