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.
@article{arxiv.2203.08047,
title = {Mobility, traffic and radio channel prediction: 5G and beyond applications},
author = {Henrik Rydén and Alex Palaios and László Hévizi and David Sandberg and Tor Kvernvik and Hamed Farhadi},
journal= {arXiv preprint arXiv:2203.08047},
year = {2022}
}