Prediction and exposure of delays from a base station perspective in 5G and beyond networksShow others and affiliations
2022 (English)In: 5G-MeMU 2022 - Proceedings of the ACM SIGCOMM 2022 Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases - Part of SIGCOMM 2022, Association for Computing Machinery, Inc , 2022, p. 8-14Conference paper, Published paper (Refereed)
Abstract [en]
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.
Place, publisher, year, edition, pages
Association for Computing Machinery, Inc , 2022. p. 8-14
Keywords [en]
5G, delay prediction, machine learning, measurements, 5G mobile communication systems, Forecasting, Millimeter waves, Configuration and managements, Delay predictions, Exposure system, Inherent flexibility, Machine-learning, Management complexity, Mm waves, Performance, Performance outcome, Base stations
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ri:diva-60264DOI: 10.1145/3538394.3546039Scopus ID: 2-s2.0-85138281433ISBN: 9781450393935 (print)OAI: oai:DiVA.org:ri-60264DiVA, id: diva2:1702344
Conference
2022 ACM SIGCOMM Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases, 5G-MeMU 2022, co-located with ACM SIGCOMM 2022, 22 August 2022
Note
Funding details: 101015922; Funding details: Stiftelsen för Strategisk Forskning, SSF, GMT-14-0032, RIT15-0075; Funding details: VINNOVA, C2020/2-2; Funding details: Bundesministerium für Bildung und Forschung, BMBF; Funding text 1: Finally, the project has also been supported by the European Union's Horizon 2020 AIatEDGE (grant agreement No. 101015922).; Funding text 2: This research has been supported by the Swedish Governmental Agency for Innovation Systems (VINNOVA) through the project Celtic IMMINENCE (C2020/2-2), the Swedish Foundation for Strategic Research (SSF) through the project Future Factories in the Cloud (GMT-14-0032) and the project Time Critical Clouds (RIT15-0075), and by the Federal Ministry of Education and Research of Germany in the programme of "Souverän. Digital. Vernetzt." joint project 6G-RIC, PIN 16KISK027. Finally, the project has also been supported by the European Union’s Horizon 2020 AIatEDGE (grant agreement No. 101015922).
2022-10-102022-10-102023-05-08Bibliographically approved