Next Generation Mobile Networks' Enablers: Machine Learning-Assisted Mobility, Traffic, and Radio Channel Prediction

Abstract

Machine learning (ML) is an important component for enabling automation in radio access networks (RANs). The work on applying ML for RAN has been under development for several years and is now also drawing attention in 3GPP standardization fora. A key component of multiple features, highlighted in the recent 3GPP specification work, is the use of mobility, traffic and radio channel prediction. These types of predictions form intelligence enablers to leverage the potentials of ML for RAN enhancements, in both current and future wireless networks. Our contributions are twofold, first we provide an overview with representative evaluation results of current applications that utilize these intelligence enablers. Next, we discuss how those enablers likely will be a cornerstone for emerging 6G use cases such as wireless energy harvesting. As the journey to 6G remains an open research area, we highlight how the development of these enablers can unlock new features in future mobile networks.