QoE Enhancement in Next Generation Wireless Ecosystems: A Machine Learning Approach

  title={QoE Enhancement in Next Generation Wireless Ecosystems: A Machine Learning Approach},
  author={Eva Ibarrola and Mark Davis and Camille Voisin and Ciara Close and Leire Cristobo},
  journal={IEEE Communications Standards Magazine},
Next-generation wireless ecosystems are expected to comprise heterogeneous technologies and diverse deployment scenarios. Ensuring quality of service (QoS) will be one of the major challenges on account of a variety of factors that are beyond the control of network and service providers in these environments. In this context, ITU-T is working on defining new Recommendations related to QoS and users' quality of experience (QoE) for the 5G era. Considering the new ITU-T QoS framework, we propose… 

Figures from this paper

QoE Evaluation for Adaptive Video Streaming: Enhanced MDT with Deep Learning

This paper proposes an architecture for performing virtual drive tests by facilitating radio-quality data from the user equipment by collecting network and application KPI at different geographical locations and at various times of the day for training an initial learning model.

Intelligent Wireless Networks: Challenges and Future Research Topics

A focus is put on discussion and analysis of recent research trends and challenges that remain open and require further research and exploration on cognitive, self-organized, and Software-defined networks.

Virtual Drive-Tests: A Case for Predicting QoE in Adaptive Video Streaming

This work develops a flexible architecture for detecting anomalies in adaptive video streaming comprising three main components: a pattern recognizer that learns a typical pattern for video quality from the client-side application traces of a specific reference video, a predictor for mapping Radio Frequency performance indicators collected on the networkside using user-based traces to a video quality measure and an anomaly detector.

Paving the Way for Distributed Artificial Intelligence Over the Air

A generic system design and an associated simulator that can be set according to wireless channels and system-level configurations are proposed to accelerate the development of DAI in wireless communication networks.

A Centralized Win-Win Cooperative Framework for Wi-Fi and 5G Radio Access Networks

This paper proposes a centralized framework that is aimed at providing a “win-win” cooperation among Wi-Fi and cellular networks, which takes into account 5G technologies and users’ requirements in terms of Quality of Service (QoS).



Machine Learning Paradigms for Next-Generation Wireless Networks

The goal is to assist the readers in refining the motivation, problem formulation, and methodology of powerful machine learning algorithms in the context of future networks in order to tap into hitherto unexplored applications and services.

A wireless traffic probe for radio resource management and QoS provisioning in IEEE 802.11 WLANs

This paper describes a wireless traffic probe for IEEE 802.11 WLANs capable of obtaining information and presenting it in a compact and intuitive format and shows how the wireless stations interact with one another in competing for the resources of the WLAN in a clear and quantifiable way.

Survey on machine learning-based QoE-QoS correlation models

  • Sana AroussiA. Mellouk
  • Computer Science
    2014 International Conference on Computing, Management and Telecommunications (ComManTel)
  • 2014
An overview of QoE-QoS correlation models based on machine learning techniques is presented and a categorization of correlation models is proposed according to the learning type.

A new global quality of service model: QoXphere

A new and integrated QoS model (QoXphere) that is spherical, adaptive, and multi-layered is presented that helps to integrate all aspects of quality in the telecommunications sector.


A framework for modelling mobile network QoE using the big data analytics approach is proposed to enable the mobile network operators effectively to manage the network performance and provide the users a satisfactory mobile InternetQoE.

Requirements of Machine Learning Based QoS Assurance for IMT-2020 Network

  • 2018.
  • 2018

QoS Functional Architecture for the IMT-2020 Network

  • 2019.
  • 2019

Business Oriented Key Performance Indicators for Management of Networks and Services

  • Geneva, Switzerland, 2006. Figure 6. KQI relevance in different scenarios. Importance of QoS criteria for users Commercial Campus Residential IEEE Communications Standards Magazine • September 2019 70
  • 2006

Quality of Service Aspects for Popular Services in Mobile Networks

  • 2014.
  • 2014

Terms and Definitions for IMT-2020 Network

  • 2017.
  • 2017