|Autors: Kougioumtzidis, G., Poulkov, V. K., Zaharis, Z., Lazaridis, P.|
Title: Machine Learning for QoE Management in Future Wireless Networks
Keywords: Machine learning; Wireless networks; Communication service; End-users; Future wireless networks; Machine-learning; Monitoring and evaluations; Network performance evaluation; Network performance monitoring; Performance; Quality of experience managements; Service provider; Quality of service
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.
1. Li, Y., Wang, Z., Li, Y., Fang, L., "Trusted distributed QoE learning model based on alliance reputation chain", Proceedings of SPIE - The International Society for Optical Engineering, vol. 12178, 2022, DOI: 10.1117/12.2631909. - 2022 - в издания, индексирани в Scopus или Web of Science
Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus