Prediction and exposure of delays from a base station perspective in 5G and beyond networks

Akhila Rao, William Tärneberg, Emma Fitzgerald, Lorenzo Corneo, Aleksandr Zavodovski, Omkar Rai, Sixten Johansson, Viktor Berggren, Hassam Riaz, Caner Kilinc, Andreas Johnsson

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)

Abstract

The inherent flexibility of 5G networks come with a high degree of configuration and management complexity. This makes the performance outcome for UEs, more than ever, dependent on intricate configurations and interplay between algorithms at various network components. In this paper, we take initial steps towards a performance exposure system at the base station using a data-driven approach for predicting performance violations in terms of RTT, as observed by the UE, in a 5G mmWave network. We present ML models to predict RTT using low-level and high-frequency base station metrics from a 5G mmWave testbed based on commercially available equipment. Predicting UE performance from a base station perspective, and exposing this knowledge, is valuable for applications to proactively address performance violations. We also compare several methods for feature reduction, which have a significant impact on monitoring load. We demonstrate our model's ability to identify RTT violations, paving the way for network providers towards an intelligent performance exposure system.

Original languageEnglish
Title of host publication5G-MeMU 2022 - Proceedings of the ACM SIGCOMM 2022 Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases - Part of SIGCOMM 2022
PublisherACM
Pages8-14
Number of pages7
ISBN (Electronic)978-1-4503-9393-5
DOIs
Publication statusPublished - 22 Aug 2022
MoE publication typeA4 Conference publication
EventACM SIGCOMM Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases - Amsterdam, Netherlands
Duration: 22 Aug 202222 Aug 2022

Conference

ConferenceACM SIGCOMM Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases
Abbreviated title5G-MeMU
Country/TerritoryNetherlands
CityAmsterdam
Period22/08/202222/08/2022

Keywords

  • 5G
  • delay prediction
  • machine learning
  • measurements

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