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

TL;DR

VANET simulators often misrepresent packet delivery in urban environments due to complex propagation. The authors calibrate an OMNeT++/Veins based DSRC channel model by layering $Lognormal$ slow fading and $Nakagami$ fast fading on top of the baseline path loss, using field data from the MLK Smart Corridor and a Genetic Algorithm to minimize the $RMSE$ between simulated and measured $PDR$, reporting over 5,500 simulation runs. The key contributions are a calibrated Lognormal–Nakagami channel model for Veins, a GA-driven parameter-tuning workflow, and validation showing $RMSE$ reduction to about $0.908$ with improved PDR alignment relative to testbed data, supplemented by heatmap analyses of spatial delivery density. The results demonstrate a practical pathway to more realistic urban VANET simulations and highlight directions for future enhancements such as shadowing by buildings and multi-path effects in Veins and similar simulators. This work enables more reliable transfer from simulation to real deployments and informs the design of next-generation VANET simulators that can cascade slow- and fast-fading models.

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.

Figures (9)

• Figure 1: Map of the testbed in Downtown Chattanooga, TN. Capabilities of the testbed including the sensors, wireless communication capabilities, and computing resources are shown in the box on the left corner. Actual pictures of the testbed are shown on the right. Location of the dual-unit (DSRC and C-V2X RSUs) are marked on the map
• Figure 2: Architecture of Veins simulation platform. Adopted from weber2021vanet
• Figure 3: Flowchart of a Genetic Algorithm
• Figure 4: The OBU-equipped vehicle used for data collection.
• Figure 5: The collected data format