Variational Autoencoder Assisted Neural Network Likelihood RSRP Prediction Model

TL;DR

This paper introduces a novel neural network model that combines variational autoencoders and likelihood modeling to improve RSRP prediction accuracy using MDT data and digital twins, addressing data collection limitations.

Contribution

It proposes a two-tier neural network leveraging VAE for environmental feature extraction and likelihood modeling for RSRP prediction, enhancing accuracy over existing models.

Findings

20% accuracy improvement over empirical models

10% accuracy improvement over fully connected networks

Effective use of real-world MDT data and digital twins

Abstract

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.