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

## TL;DR

This paper presents a real-world study on using machine learning for predicting network quality in vehicular multi-MNO networks, demonstrating benefits and highlighting the importance of MNO-specific models.

## Contribution

It provides empirical analysis of multi-MNO network prediction performance and emphasizes the need for tailored machine learning models for different network configurations.

## Key findings

- Multi-MNO approaches improve network quality indicators.
- Single dominant MNO still benefits from multi-MNO strategies.
- MNO-specific models are crucial for accurate predictions.

## 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.