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

doi:10.1145/3538394.3546039

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

Department of Computer Science

KTH Royal Institute of Technology
Lund University
Max Planck Institute for Informatics
Ericsson AB
University of Edinburgh
Uppsala University

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

9 Citations (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 language	English
Title of host publication	5G-MeMU 2022 - Proceedings of the ACM SIGCOMM 2022 Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases - Part of SIGCOMM 2022
Publisher	ACM
Pages	8-14
Number of pages	7
ISBN (Electronic)	978-1-4503-9393-5
DOIs	https://doi.org/10.1145/3538394.3546039
Publication status	Published - 22 Aug 2022
MoE publication type	A4 Conference publication
Event	ACM SIGCOMM Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases - Amsterdam, Netherlands Duration: 22 Aug 2022 → 22 Aug 2022

Workshop

Workshop	ACM SIGCOMM Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases
Abbreviated title	5G-MeMU
Country/Territory	Netherlands
City	Amsterdam
Period	22/08/2022 → 22/08/2022

Funding

Finally, the project has also been supported by the European Union's Horizon 2020 AIatEDGE (grant agreement No. 101015922). 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).

Keywords

5G
delay prediction
machine learning
measurements

Access to Document

10.1145/3538394.3546039

Cite this

Rao, A., Tärneberg, W., Fitzgerald, E., Corneo, L., Zavodovski, A., Rai, O., Johansson, S., Berggren, V., Riaz, H., Kilinc, C., & Johnsson, A. (2022). Prediction and exposure of delays from a base station perspective in 5G and beyond networks. 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 (pp. 8-14). ACM. https://doi.org/10.1145/3538394.3546039

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title = "Prediction and exposure of delays from a base station perspective in 5G and beyond networks",

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.",

keywords = "5G, delay prediction, machine learning, measurements",

author = "Akhila Rao and William T{\"a}rneberg and Emma Fitzgerald and Lorenzo Corneo and Aleksandr Zavodovski and Omkar Rai and Sixten Johansson and Viktor Berggren and Hassam Riaz and Caner Kilinc and Andreas Johnsson",

note = "Funding Information: 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 {"}Souver{\"a}n. Digital. Vernetzt.{"} joint project 6G-RIC, PIN 16KISK027. Finally, the project has also been supported by the European Union{\textquoteright}s Horizon 2020 AIatEDGE (grant agreement No. 101015922). Publisher Copyright: {\textcopyright} 2022 ACM.; ACM SIGCOMM Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases, 5G-MeMU ; Conference date: 22-08-2022 Through 22-08-2022",

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doi = "10.1145/3538394.3546039",

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Rao, A, Tärneberg, W, Fitzgerald, E, Corneo, L, Zavodovski, A, Rai, O, Johansson, S, Berggren, V, Riaz, H, Kilinc, C & Johnsson, A 2022, Prediction and exposure of delays from a base station perspective in 5G and beyond networks. 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. ACM, pp. 8-14, ACM SIGCOMM Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases, Amsterdam, Netherlands, 22/08/2022. https://doi.org/10.1145/3538394.3546039

Prediction and exposure of delays from a base station perspective in 5G and beyond networks. / Rao, Akhila; Tärneberg, William; Fitzgerald, Emma et al.
5G-MeMU 2022 - Proceedings of the ACM SIGCOMM 2022 Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases - Part of SIGCOMM 2022. ACM, 2022. p. 8-14.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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AU - Rao, Akhila

AU - Tärneberg, William

AU - Fitzgerald, Emma

AU - Corneo, Lorenzo

AU - Zavodovski, Aleksandr

AU - Rai, Omkar

AU - Johansson, Sixten

AU - Berggren, Viktor

AU - Riaz, Hassam

AU - Kilinc, Caner

AU - Johnsson, Andreas

N1 - Funding Information: 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). Publisher Copyright: © 2022 ACM.

PY - 2022/8/22

Y1 - 2022/8/22

N2 - 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.

AB - 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.

KW - 5G

KW - delay prediction

KW - machine learning

KW - measurements

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DO - 10.1145/3538394.3546039

M3 - Conference article in proceedings

AN - SCOPUS:85138281433

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BT - 5G-MeMU 2022 - Proceedings of the ACM SIGCOMM 2022 Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases - Part of SIGCOMM 2022

PB - ACM

T2 - ACM SIGCOMM Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases

Y2 - 22 August 2022 through 22 August 2022

ER -

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

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Workshop

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Keywords

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