Machine Learning for QoE Management in Future Wireless Networks

Georgios Kougioumtzidis; Vladimir Poulkov; Zaharias Zaharis; Pavlos Lazaridis

doi:10.23919/URSIGASS51995.2021.9560226

Machine Learning for QoE Management in Future Wireless Networks

Georgios Kougioumtzidis, Vladimir Poulkov, Zaharias Zaharis, Pavlos Lazaridis

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

The growth in volume and heterogeneity of accessible services in future wireless networks (FWNs), imposes pressure to communication service providers (CSPs) to expand their capacity for network performance monitoring and evaluation, in particular in terms of the performance as it is perceived by end-users. The quality of experience (QoE)-aware design model allows to understand and analyze the operation of networks and services from the end-user's perspective. In addition, network measurements based on QoE constitute a key source of knowledge for the overall functionality and management of the network. In this respect, the implementation of artificial intelligence (AI) and machine learning (ML) in QoE management, increases the accuracy of modeling procedures, improves the monitoring process efficiency, and develops innovative optimization and control methodologies.

Original language	English
Title of host publication	2021 34th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Number of pages	4
ISBN (Electronic)	9789463968027
ISBN (Print)	9781665429955
DOIs	https://doi.org/10.23919/URSIGASS51995.2021.9560226
Publication status	Published - 14 Oct 2021
Event	34th General Assembly and Scientific Symposium of the International Union of Radio Science - Rome (Virtual), Italy Duration: 28 Aug 2021 → 4 Sep 2021 Conference number: 34 https://www.ursi2021.org/ https://ieeexplore.ieee.org/xpl/conhome/9560113/proceeding

Publication series

Name	2021 34th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2021
ISSN (Print)	2640-7027
ISSN (Electronic)	2642-4339

Conference

Conference	34th General Assembly and Scientific Symposium of the International Union of Radio Science
Abbreviated title	URSI GASS 2021
Country/Territory	Italy
City	Rome (Virtual)
Period	28/08/21 → 4/09/21
Internet address	https://www.ursi2021.org/ https://ieeexplore.ieee.org/xpl/conhome/9560113/proceeding

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.23919/URSIGASS51995.2021.9560226Licence: Unspecified

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Kougioumtzidis, G., Poulkov, V., Zaharis, Z., & Lazaridis, P. (2021). Machine Learning for QoE Management in Future Wireless Networks. In 2021 34th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2021 [9560226] (2021 34th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/URSIGASS51995.2021.9560226

Kougioumtzidis, Georgios ; Poulkov, Vladimir ; Zaharis, Zaharias et al. / Machine Learning for QoE Management in Future Wireless Networks. 2021 34th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (2021 34th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2021).

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title = "Machine Learning for QoE Management in Future Wireless Networks",

abstract = "The growth in volume and heterogeneity of accessible services in future wireless networks (FWNs), imposes pressure to communication service providers (CSPs) to expand their capacity for network performance monitoring and evaluation, in particular in terms of the performance as it is perceived by end-users. The quality of experience (QoE)-aware design model allows to understand and analyze the operation of networks and services from the end-user's perspective. In addition, network measurements based on QoE constitute a key source of knowledge for the overall functionality and management of the network. In this respect, the implementation of artificial intelligence (AI) and machine learning (ML) in QoE management, increases the accuracy of modeling procedures, improves the monitoring process efficiency, and develops innovative optimization and control methodologies.",

keywords = "Knowledge engineering, Technological innovations, Analytical models, Wireless networks, Network slicing, Process control, Machine learning",

author = "Georgios Kougioumtzidis and Vladimir Poulkov and Zaharias Zaharis and Pavlos Lazaridis",

note = "Funding Information: This work was supported by the European Union, through the Horizon 2020 Marie Skodowska-Curie Innovative Training Networks Programme “Mobility and Training for beyond 5G Ecosystems (MOTOR5G)” under grant agreement no. 861219. Publisher Copyright: {\textcopyright} 2021 URSI. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 34th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2021 ; Conference date: 28-08-2021 Through 04-09-2021",

year = "2021",

month = oct,

day = "14",

doi = "10.23919/URSIGASS51995.2021.9560226",

language = "English",

isbn = "9781665429955",

series = "2021 34th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2021 34th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2021",

address = "United States",

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Kougioumtzidis, G, Poulkov, V, Zaharis, Z & Lazaridis, P 2021, Machine Learning for QoE Management in Future Wireless Networks. in 2021 34th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2021., 9560226, 2021 34th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2021, Institute of Electrical and Electronics Engineers Inc., 34th General Assembly and Scientific Symposium of the International Union of Radio Science, Rome (Virtual), Italy, 28/08/21. https://doi.org/10.23919/URSIGASS51995.2021.9560226

Machine Learning for QoE Management in Future Wireless Networks. / Kougioumtzidis, Georgios; Poulkov, Vladimir; Zaharis, Zaharias et al.

2021 34th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2021. Institute of Electrical and Electronics Engineers Inc., 2021. 9560226 (2021 34th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Machine Learning for QoE Management in Future Wireless Networks

AU - Kougioumtzidis, Georgios

AU - Poulkov, Vladimir

AU - Zaharis, Zaharias

AU - Lazaridis, Pavlos

N1 - Conference code: 34

PY - 2021/10/14

Y1 - 2021/10/14

N2 - The growth in volume and heterogeneity of accessible services in future wireless networks (FWNs), imposes pressure to communication service providers (CSPs) to expand their capacity for network performance monitoring and evaluation, in particular in terms of the performance as it is perceived by end-users. The quality of experience (QoE)-aware design model allows to understand and analyze the operation of networks and services from the end-user's perspective. In addition, network measurements based on QoE constitute a key source of knowledge for the overall functionality and management of the network. In this respect, the implementation of artificial intelligence (AI) and machine learning (ML) in QoE management, increases the accuracy of modeling procedures, improves the monitoring process efficiency, and develops innovative optimization and control methodologies.

AB - The growth in volume and heterogeneity of accessible services in future wireless networks (FWNs), imposes pressure to communication service providers (CSPs) to expand their capacity for network performance monitoring and evaluation, in particular in terms of the performance as it is perceived by end-users. The quality of experience (QoE)-aware design model allows to understand and analyze the operation of networks and services from the end-user's perspective. In addition, network measurements based on QoE constitute a key source of knowledge for the overall functionality and management of the network. In this respect, the implementation of artificial intelligence (AI) and machine learning (ML) in QoE management, increases the accuracy of modeling procedures, improves the monitoring process efficiency, and develops innovative optimization and control methodologies.

KW - Knowledge engineering

KW - Technological innovations

KW - Analytical models

KW - Wireless networks

KW - Network slicing

KW - Process control

KW - Machine learning

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DO - 10.23919/URSIGASS51995.2021.9560226

M3 - Conference contribution

AN - SCOPUS:85118236570

SN - 9781665429955

T3 - 2021 34th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2021

BT - 2021 34th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 34th General Assembly and Scientific Symposium of the International Union of Radio Science

Y2 - 28 August 2021 through 4 September 2021

ER -

Kougioumtzidis G, Poulkov V, Zaharis Z, Lazaridis P. Machine Learning for QoE Management in Future Wireless Networks. In 2021 34th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2021. Institute of Electrical and Electronics Engineers Inc. 2021. 9560226. (2021 34th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2021). https://doi.org/10.23919/URSIGASS51995.2021.9560226

Machine Learning for QoE Management in Future Wireless Networks

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UN SDGs

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