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

Tietotekniikan laitos

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

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

9 Sitaatiot (Scopus)

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	5G-MeMU 2022 - Proceedings of the ACM SIGCOMM 2022 Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases - Part of SIGCOMM 2022
Kustantaja	ACM
Sivut	8-14
Sivumäärä	7
ISBN (elektroninen)	978-1-4503-9393-5
DOI - pysyväislinkit	https://doi.org/10.1145/3538394.3546039
Tila	Julkaistu - 22 elok. 2022
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	ACM SIGCOMM Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases - Amsterdam, Alankomaat Kesto: 22 elok. 2022 → 22 elok. 2022

Workshop

Workshop	ACM SIGCOMM Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases
Lyhennettä	5G-MeMU
Maa/Alue	Alankomaat
Kaupunki	Amsterdam
Ajanjakso	22/08/2022 → 22/08/2022

Rahoitus

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).

Pääsy asiakirjaan

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. teoksessa 5G-MeMU 2022 - Proceedings of the ACM SIGCOMM 2022 Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases - Part of SIGCOMM 2022 (Sivut 8-14). ACM. https://doi.org/10.1145/3538394.3546039

@inproceedings{791b38f0f4f94811a4204a651928feb0,

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",

year = "2022",

month = aug,

day = "22",

doi = "10.1145/3538394.3546039",

language = "English",

pages = "8--14",

booktitle = "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",

address = "United States",

}

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. julkaisussa 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, Sivut 8-14, ACM SIGCOMM Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases, Amsterdam, Alankomaat, 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. s. 8-14.

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - Prediction and exposure of delays from a base station perspective in 5G and beyond networks

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

UR - https://www.scopus.com/pages/publications/85138281433

U2 - 10.1145/3538394.3546039

DO - 10.1145/3538394.3546039

M3 - Conference article in proceedings

AN - SCOPUS:85138281433

SP - 8

EP - 14

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 -

Rao A, Tärneberg W, Fitzgerald E, Corneo L, Zavodovski A, Rai O et al. Prediction and exposure of delays from a base station perspective in 5G and beyond networks. julkaisussa 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. s. 8-14 doi: 10.1145/3538394.3546039

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

Abstrakti

Workshop

Rahoitus

Pääsy asiakirjaan

Muut tiedostot ja linkit

Sormenjälki

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