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. I. INTRODUCTION Anticipatory communication [1] has been proposed as a method to face the various challenges of next-generation networks, which are related to higher requirements (e.g., latency and data rate as enablers for autonomous driving) as well as massive increases in the overall data usage [2] . This novel communication paradigm aims to optimize decision processes within communication networks by proactively integrating context information such as mobility predictions and network quality parameters. Applications range from opportunistic sensor data transfer for optimizing the coexistence of different cell users [3] to reliable and mobility-aware multihop routing [4] . Since anticipatory communications is a datadriven approach, it relies on knowledge and prediction models. In recent work [5] , we have demonstrated the potentials of using machine learning-based data rate prediction and multilayer connectivity maps for improving the resource-efficiency of vehicular sensor data transmissions. In this paper, we provide an empirical analysis of the potentials of using data rate prediction within multi-connectivity cellular vehicular networks. The real world evaluation is carried out in the public cellular networks of the three MNOs in Germany and paves the way for future development of context-aware multi-connectivity optimizations. It furthermore shows how upcoming 5G-based vehicular communication systems can exploit multi-connectivity for compensating network coverage gaps. Fig. 1 shows an example trace of the Long Term Evolution (LTE) Reference Signal Received Power (RSRP) for three different MNOs during a drive test in a suburban environment. It can be seen that although not all carriers are able to guarantee seamless connectivity for the whole experiment
• As discussed in REF , the prediction accuracy highly depends on the payload size of the to be transmitted data packet.
Empirical Analysis of Client-based Network Quality Prediction in Vehicular Multi-MNO Networks
IEEE 90th Vehicular Technology Conference (VTC2019-Fall)
Computer Science