Analytical Models of the Performance of IEEE 802.11p Vehicle to Vehicle Communications
Not on the map. Why?
Abstract (arXiv)
The critical nature of vehicular communications requires their extensive testing and evaluation. Analytical models can represent an attractive and cost-effective approach for such evaluation if they can adequately model all underlying effects that impact the performance of vehicular communications. Several analytical models have been proposed to date to model vehicular communications based on the IEEE 802.11p (or DSRC) standard. However, existing models normally model in detail the MAC (Medium Access Control), and generally simplify the propagation and interference effects. This reduces their value as an alternative to evaluate the performance of vehicular communications. This paper addresses this gap, and presents new analytical models that accurately model the performance of vehicle-to-vehicle communications based on the IEEE 802.11p standard. The models jointly account for a detailed modeling of the propagation and interference effects, as well as the impact of the hidden terminal problem. The model quantifies the PDR (Packet Delivery Ratio) as a function of the distance between transmitter and receiver. The paper also presents new analytical models to quantify the probability of the four different types of packet errors in IEEE 802.11p. In addition, the paper presents the first analytical model capable to accurately estimate the Channel Busy Ratio (CBR) metric even under high channel load levels. All the analytical models are validated by means of simulation for a wide range of parameters, including traffic densities, packet transmission frequencies, transmission power levels, data rates and packet sizes. An implementation of the models is provided openly to facilitate their use by the community.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Apr 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 1; 2024: 2; 2025: 1; 2026: 2.
6 papers.
- 2023Vehicle-to-Everything Cooperative Perception for Autonomous Drivingcited by 15
- 2024Hierarchical Federated Learning in Multi-hop Cluster-Based VANETscited by 7
- 2025Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networkscited by 1
- 2024Secure Hierarchical Federated Learning in Vehicular Networks Using Dynamic Client Selection and Anomaly Detectioncited by 0
- 2026Hierarchical Multi-Task Federated Learning in VANETscited by 0
- 2026Encoder-Sharing Hierarchical Federated Multi-Task Learning for VANETscited by 0
- 2026Encoder-Sharing Hierarchical Federated Multi-Task Learning for VANETs28 Sep 2026
- 2026Hierarchical Multi-Task Federated Learning in VANETs8 Aug 2026
- 2025Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networks2 May 2025
- 2024Secure Hierarchical Federated Learning in Vehicular Networks Using Dynamic Client Selection and Anomaly Detection25 May 2024
- 2024Hierarchical Federated Learning in Multi-hop Cluster-Based VANETs18 Jan 2024
- 2023Vehicle-to-Everything Cooperative Perception for Autonomous Driving5 Oct 2023