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

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 $\mathrm{5 0, 0 0 0} \mathrm{5 G}$ 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 ($\mathrm{0 . 2 5}$ vs. $\mathrm{0 . 4 5}$ for fixed-configuration MLP-based models).