Panek, MichałMichałPanekJablonski, IreneuszIreneuszJablonskiWozniak, MichalMichalWozniak2025-02-112025-02-112024https://publica.fraunhofer.de/handle/publica/48379910.1109/PIMRC59610.2024.108173032-s2.0-85216013568Mobile 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 50,0005G 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).enfalseconference paper