Measuring customer experience on mobile data is of utmost importance for global mobile operators. The reference signal received power (RSRP) is one of the important indicators for current mobile network management, evaluation and monitoring. Radio data gathered through the minimization of drive test (MDT), a 3GPP standard technique, is commonly used for radio network analysis. Collecting MDT data in different geographical areas is inefficient and constrained by the terrain conditions and user presence, hence is not an adequate technique for dynamic radio environments. In this paper, we study a generative model for RSRP prediction, exploiting MDT data and a digital twin (DT), and propose a data-driven, two-tier neural network (NN) model. In the first tier, environmental information related to user equipment (UE), base stations (BS) and network key performance indicators (KPI) are extracted through a variational autoencoder (VAE). The second tier is designed as a likelihood model. Here, the environmental features and real MDT data features are adopted, formulating an integrated training process. On validation, our proposed model that uses real-world data demonstrates an accuracy improvement of about 20% or more compared with the empirical model and about 10% when compared with a fully connected prediction network.

翻译：测量移动数据方面的客户经验对于全球移动运营商至关重要。基准信号收到的电力(RSRP)是当前移动网络管理、评价和监测的重要指标之一。通过最大程度的驱动测试(MDT)收集的无线电数据(MDT,3GPP标准技术)通常用于无线电网络分析。在不同地理区域收集MDT数据效率低下,受地形条件和用户存在的制约,因此不是动态无线电环境的适当技术。在本文中,我们研究RSRP预测的基因化模型,利用MDT数据和数字双对子(DT),并提出数据驱动的双层神经网络(NNN)模型。在第一级,与用户设备(UE)、基站(BS)和网络关键业绩指标(KPI)有关的环境信息是通过变式自动电解码(VAE)提取的。第二层设计为一种可能性模型。在这里,采用了环境特征和真正的MDT数据特征,制定了一个综合培训进程。在论证方面,我们提议的模型使用真实世界数据表明与完全连接的模型和10 %的预测相比,精确度提高了大约20%或更多。