Improving VANET Simulation Channel Model in an Urban Environment via Calibration Using Real-World Communication Data

TL;DR

This paper enhances VANET simulation accuracy by calibrating a channel model with real-world urban data using genetic algorithms, leading to more realistic packet delivery predictions in vehicular communication networks.

Contribution

It introduces a calibration method for VANET channel models using real urban data and genetic algorithms, improving simulation fidelity over default models.

Findings

Calibrated model outperforms default in packet delivery accuracy.

Genetic Algorithm effectively tunes physical layer parameters.

Improved simulation matches real-world urban VANET data better.

Abstract

Wireless communication channels in Vehicular Ad-hoc NETworks (VANETs) suffer from packet losses, which severely influences the performance of their applications. There are several reasons for this loss, including but not limited to signal interference with itself after being reflected from the ground and other objects, the doppler effect caused by the speed of the vehicle, and buildings and other vehicles blocking the signal. As a result, VANET simulators must be calibrated in order to mimic the behavior of real-world vehicular communication channels effectively. In this paper, we calibrated an OMNET++(Objective Modular Network Testbed in C++)/Veins simulator for VANET's dedicated short-range communications (DSRC) protocol using the field data from the urban testbed in Downtown Chattanooga, TN. Channel propagation models, as well as physical layer parameters, were calibrated using a Genetic Algorithm (GA). The performance of the calibrated simulator was improved significantly in comparison with the default settings in Veins. The final results were compared to the real-world data collected from the testbed and performance shows that the final calibrated channel model performs better than uncalibrated models in simulating the packet delivery pattern of DSRC channels.