Modeling configuration-performance relation in a mobile network: a data-driven approach
<|reference_start|>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 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).<|reference_end|>
@article{panek2024modeling,
    title={Modeling configuration-performance relation in a mobile network: a
  data-driven approach},
    author={Micha{l} Panek, Ireneusz Jab{l}o'nski, Micha{l} Wo'zniak},
    journal={arXiv preprint arXiv:2407.06702},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06702},
    primaryClass={cs.NI}
}