Empirical Analysis of Client-based Network Quality Prediction in Vehicular Multi-MNO Networks

Abstract

Multi-Mobile Network Operator (MNO) networking is a promising method to exploit the joint force of multiple available cellular data connections within vehicular networks. By applying anticipatory communication principles, data transmissions can dynamically utilize the mobile network with the highest estimated network performance in order to achieve improvements in data rate, resource efficiency, and reliability. In this paper, we present the results of a comprehensive real-world measurement campaign in public cellular networks in different scenarios and analyze the performance of online data rate prediction based on multiple machine learning models and data aggregation strategies. It is shown that multi-MNO approaches are able to achieve significant benefits for all considered network quality and end-to-end indicators even in the presence of a single dominant MNO. However, the analysis points out that anticipatory multi-MNO communication requires the consideration of MNO-specific machine learning models since the impact of the different features is highly depending on the configuration of the network infrastructure.

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• Boosting Vehicle-to-cloud Communication by Machine Learning-enabled Context Prediction
• Data-driven Network Simulation for Performance Analysis of Anticipatory Vehicular Communication Systems
• Towards Data-driven Simulation of End-to-end Network Performance Indicators
• Acting Selfish for the Good of All: Contextual Bandits for Resource-Efficient Transmission of Vehicular Sensor Data
• Client-Based Intelligence for Resource Efficient Vehicular Big Data Transfer in Future 6G Network