Data-driven Network Simulation for Performance Analysis of Anticipatory Vehicular Communication Systems

TL;DR

This paper introduces Data-driven Network Simulation (DDNS), a machine learning-based approach that enhances the accuracy and speed of simulating anticipatory vehicular communication systems, bridging the gap between real-world behavior and traditional simulations.

Contribution

The paper presents a novel data-driven simulation method combining machine learning models to better replicate real-world vehicular communication system behavior.

Findings

DDNS achieves higher accuracy compared to traditional network simulations.

DDNS provides significantly faster results than real-world experiments.

The approach effectively models cross-layer dependencies in vehicular networks.

Abstract

The provision of reliable connectivity is envisioned as a key enabler for future autonomous driving. Anticipatory communication techniques have been proposed for proactively considering the properties of the highly dynamic radio channel within the communication systems themselves. Since real world experiments are highly time-consuming and lack a controllable environment, performance evaluations and parameter studies for novel anticipatory vehicular communication systems are typically carried out based on network simulations. However, due to the required simplifications and the wide range of unknown parameters (e.g., Mobile Network Operator (MNO)-specific configurations of the network infrastructure), the achieved results often differ significantly from the behavior in real world evaluations. In this paper, we present Data-driven Network Simulation (DDNS) as a novel data-driven approach for analyzing and optimizing anticipatory vehicular communication systems. Different machine learning models are combined for achieving a close to reality representation of the analyzed system's behavior. In a proof of concept evaluation focusing on opportunistic vehicular data transfer, the proposed method is validated against field measurements and system-level network simulation. In contrast to the latter, DDNS does not only provide massively faster result generation, it also achieves a significantly better representation of the real world behavior due to implicit consideration of cross-layer dependencies by the machine learning approach.