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. By implementing this framework, we mitigate the concept drift of the pQoS model induced by variations in the propagation environment and interference. Each predictive cluster model is locally trained on vehicles traveling within the cluster boundaries using federated learning. A significant challenge is balancing the trade-off between the number of clusters, prediction accuracy, and communication overhead for updating local models. This trade-off suggests the novel problem of performing a joint optimization of the training and number of clusters. To address such difficult optimization, we propose an iterative approximate solution using proximal alternative minimization for which we provide convergence guarantees. 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.