Geometry-Based Vehicle-to-Vehicle Channel Modeling for Large-Scale Simulation
Not on the map. Why?
Abstract (arXiv)
Due to the dynamic nature of vehicular traffic and the road surroundings, vehicle-to-vehicle (V2V) propagation characteristics vary greatly on both small- and large-scale. Recent measurements have shown that both large static objects (e.g., buildings and foliage) as well as mobile objects (surrounding vehicles) have a profound impact on V2V communication. At the same time, system-level Vehicular Ad Hoc Network (VANET) simulators by and large employ simple statistical propagation models, which do not account for surrounding objects explicitly. We designed GEMV2 (Geometry-based Efficient propagation Model for V2V communication), which uses outlines of vehicles, buildings, and foliage to distinguish the following three types of links: line of sight (LOS), non-LOS due to vehicles, and non- LOS due to static objects. For each link, GEMV2 calculates the large-scale signal variations deterministically, whereas the small- scale signal variations are calculated stochastically based on the number and size of surrounding objects. We implement GEMV2 in MATLAB and show that it scales well by using it to simulate radio propagation for city-wide networks with tens of thousands of vehicles on commodity hardware. We make the source code of GEMV2 freely available. Finally, we validate GEMV2 against extensive measurements performed in urban, suburban, highway, and open space environment.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 May 2013.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 0; 2025: 1; 2026: 2.
6 papers.
- 2022Artificial Intelligence in Vehicular Wireless Networks: A Case Study Using ns-3cited by 3
- 2021Transfer Learning in Multi-Agent Reinforcement Learning with Double Q-Networks for Distributed Resource Sharing in V2X Communicationcited by 1
- 2026AI-Driven Multi-Hop Relay Selection for Smart Urban NR-V2X Networks via Learning-to-Optimize Graph Neural Networkscited by 1
- 2026LUCID: An Agentic AI Framework on Digital-Twin in the Loop for QoS-Guaranteeing Robotic Controlcited by 1
- 2021Hybrid Model and Data Driven Algorithm for Online Learning of Any-to-Any Path Loss Mapscited by 0
- 2025PRATA: A Framework to Enable Predictive QoS in Vehicular Networks via Artificial Intelligencecited by 0
- 2026LUCID: An Agentic AI Framework on Digital-Twin in the Loop for QoS-Guaranteeing Robotic Control28 Aug 2026
- 2026AI-Driven Multi-Hop Relay Selection for Smart Urban NR-V2X Networks via Learning-to-Optimize Graph Neural Networks15 Jul 2026
- 2025PRATA: A Framework to Enable Predictive QoS in Vehicular Networks via Artificial Intelligence15 Jul 2025
- 2022Artificial Intelligence in Vehicular Wireless Networks: A Case Study Using ns-310 Mar 2022
- 2021Hybrid Model and Data Driven Algorithm for Online Learning of Any-to-Any Path Loss Maps14 Jul 2021
- 2021Transfer Learning in Multi-Agent Reinforcement Learning with Double Q-Networks for Distributed Resource Sharing in V2X Communication13 Jul 2021