Vehicular Communications: Survey and Challenges of Channel and Propagation Models (1505.06004v2)

Abstract: Vehicular communication is characterized by a dynamic environment, high mobility, and comparatively low antenna heights on the communicating entities (vehicles and roadside units). These characteristics make vehicular propagation and channel modeling particularly challenging. In this article, we classify and describe the most relevant vehicular propagation and channel models, with a particular focus on the usability of the models for the evaluation of protocols and applications. We first classify the models based on the propagation mechanisms they employ and their implementation approach. We also classify the models based on the channel properties they implement and pay special attention to the usability of the models, including the complexity of implementation, scalability, and the input requirements (e.g., geographical data input). We also discuss the less-explored aspects in vehicular channel modeling, including modeling specific environments (e.g., tunnels, overpasses, and parking lots) and types of communicating vehicles (e.g., scooters and public transportation vehicles). We conclude by identifying the underresearched aspects of vehicular propagation and channel modeling that require further modeling and measurement studies.

• The paper presents a comprehensive survey of channel and propagation models tailored for vehicular communications in varied environments.
• It categorizes models into large-scale, small-scale, and geometry-based types, addressing challenges like high Doppler shifts and frequency-selective fading.
• It evaluates model usability by assessing scalability, accuracy, and practical deployment for ITS and emerging 5G-enabled vehicular applications.

Overview of Vehicular Communications Channel and Propagation Models

This paper systematically surveys channel and propagation models specific to vehicular communications, emphasizing the unique challenges posed by this dynamic and diverse environment. Vehicular communication systems, including vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-pedestrian (V2P) paradigms, encounter propagation characteristics distinct from other wireless communication systems due to their highly mobile platforms, low antenna placements, and varied environments.

The authors categorize vehicular propagation and channel models based on their underlying mechanisms and the properties they simulate. They also assess these models’ usability in terms of implementation complexity and scalability, particularly for evaluating communication protocols and applications.

Classification of Propagation Models

Vehicular communication channels present complexities such as variable path loss, high Doppler shift, and frequency-selective fading, necessitating model classifications as follows:

1. Large-scale Propagation Models: These include commonly used log-distance path loss models with adaptations for various environments, including highways and urban settings, to quantify path losses over extensive areas.
2. Small-scale Fading Models: They capture finer signal variations due to multi-path effects, typically using statistical distributions like Weibull, Nakagami, or Gaussian, with parameters derived from empirical data.
3. Geometry-Based Models:
   • Deterministic Models: Ray-tracing techniques apply detailed geometric information to vet propagation paths and channel statistics realistically.
   • Simplified Models: These leverage geometric attributes for environment-specific parameter tuning, providing accuracy without intensive computation.
4. Non-Geometry-Based Models: These are primarily statistical models adjusted based on empirical data from specific environments, offering high scalability at reduced accuracy.

Model Usability and Practical Considerations

The paper evaluates the models against several criteria crucial for protocol and application evaluations:

• Spatial and Temporal Dependency: Quality models must simulate consistent effects over time and space, reflecting realistic correlation due to static and dynamic features.
• Non-Stationarity and Environmental Extensibility: Recognizing that vehicular channels are non-stationary, models should adapt to varying conditions that might arise over transmission.
• Accuracy and Scalability: Models need to balance detailed accuracy (e.g., through ray-tracing) with scalable efficiency for extensive simulations necessary for real-world V2I and V2V deployments.

Future Directions

The deployment of ITS necessitates comprehensive models validated against measurements in real-world scenarios. The integration of vehicle-specific models beyond personal cars—such as trucks or motorcycles—is highlighted as a key area for further exploration. Additionally, under-explored areas like tunnels and multi-level highways require dedicated model development due to their unique propagation characteristics.

The paper also underscores emerging collaborations between vehicular models and 5G initiatives, which could enhance low-latency, high-mobility communication scenarios these models aim to replicate.

Conclusions

By addressing existing vehicular channel modeling gaps, this paper aids the enhancement of protocols and applications intended for rapidly evolving vehicular communication systems. It provides a foundation for ongoing research into adaptable and realistic modeling techniques, taking into account the specific requirements of vehicular networks and technologies such as 5G. These models will be indispensable for the continued development and deployment of effective cooperative intelligent transportation systems.