A Route Planning Scheme with 5G QoS Prediction Based on Probability Distribution Detection

Abstract

5G mobile communication technology can satisfy the needs of network quality of service (QoS) for vehicle-to-everything (V2X) in ideal conditions. However, complex intelligent transportation scenarios may lead to fluctuations in 5G QoS, resulting in passive and lagging degradation of the service level of V2X services. To address the challenge of aligning service requirements with network conditions, it is crucial to explore schemes for predicting and managing QoS fluctuations. This paper proposes a vehicle route planning scheme to improve the quality of experience for V2X services by QoS prediction based on probability distribution detection (PDD). We design a distribution detection algorithm to tackle the issue of improving QoS prediction accuracy by calculating probability confidence weights of the outcome of two different QoS prediction models. Simulation evaluations show that the proposed PDD-based prediction method significantly enhances the accuracy of predictions. We have achieved 0.128 mean absolute error, with 0.189 root mean square error, in predicting the network throughput. Furthermore, in comparison to the routes selected by the length-based route planning scheme, the proposed route planning strategy can enhance the network throughput by at least 5.3 kbps.