Clustering of geographical segments for predictive Quality of Service of connected vehicles

To meet the growing demands for connectivity and reliability in cellular networks, it is essential to ensure reliable quality of service (QoS) guarantees for end users. The integration of predictive QoS (pQoS) in cellular networks enables proactive fulfillment of QoS requirements for a diverse range of applications, including intelligent transportation systems. This study presents a pQoS framework in cellular networks, particularly for connected vehicles, that divides the road into segments, clusters them, and assigns a pQoS model to each cluster.

Research paper

#6G #ADAS

Ultimately, by evaluations with real-world data, our numerical findings reveal that our proposed clustered predictive model reduces the mean absolute percentage error by 8%, and the mean absolute error by 7%, compared to conventional predictive approaches proposed by prior studies.

Full abstract in IEEEXplore DOI: 10.1109/TVT.2025.3580989

Authors

Oscar Stenhammar – Ericsson Research, and KTH Royal Institute of Technology, Sweden

Gábor Fodor – Ericsson Research, Sweden

Saeed Razavikia, Carlo Fischione – KTH Royal Institute of Technology, Sweden

Published in IEEE Transactions on Vehicular Technology ( Volume: 74, Issue: 11)

©2025 IEEE. Authors may share the accepted version of their IEEE periodical articles. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse.

Download the paper

Clustering of geographical segments for predictive Quality of Service of connected vehicles

pdf