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).

翻译：移动网络性能建模通常假设基站配置固定或仅考虑有限参数，这阻碍了对多维、多样化配置空间的探索（例如用于优化目的）。本文提出一种基于神经网络架构的性能预测方法，该方法同时考虑网络基站的配置参数与网络环境条件。我们利用来自超过50,000个5G基站的数据进行了大量实验验证。评估过程将所提方法与针对固定配置开发的模型进行了对比。结果表明：结合配置参数的性能建模方法优于单一配置模型，且能够预测未在模型训练中使用的未知配置的性能。该方法实现了显著更低的平均绝对误差（基于固定配置的多层感知机模型误差为0.45，而本方法误差为0.25）。