Mobility, traffic and radio channel prediction: 5G and beyond applications

TL;DR

This paper reviews the use of machine learning for predicting mobility, traffic, and radio channels in 5G and beyond, highlighting their role in enabling intelligent RAN features and future 6G applications.

Contribution

It provides an overview and evaluation of current ML-based prediction applications in RAN, emphasizing their importance for 6G use cases like wireless energy transmission.

Findings

ML predictions enhance RAN automation and efficiency

Evaluation results demonstrate the effectiveness of current prediction models

Predictions are key enablers for future 6G applications

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 many years and is now also drawing attention in 3GPP and Open-RAN standardization fora. A key component of multiple features, also highlighted in the recent 3GPP specification work, is the use of mobility, traffic and radio channel prediction. These types of predictions form the intelligence enablers to leverage the potentials for ML for RAN, both for current and future wireless networks. This paper provides an overview with evaluation results of current applications that utilize such intelligence enablers, we then discuss how those enablers likely will be a cornerstone for emerging 6G use cases such as wireless energy transmission.