Machine Learning for QoE Management in Future Wireless Networks

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.

• Georgios Kougioumtzidis
• Vladimir Poulkov
• Zaharias Zaharis
• Pavlos Lazaridis

2021 XXXIVth General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS)

https://ieeexplore.ieee.org/document/9560226