Vehicular Blockage Modelling and Performance Analysis for mmWave V2V Communications
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Abstract (arXiv)
Vehicle-to-Everything (V2X) communications are revolutionizing the connectivity of transportation systems supporting safe and efficient road mobility. To meet the growing bandwidth eagerness of V2X services, millimeter-wave (e.g., 5G new radio over spectrum 26.50 - 48.20 GHz) and sub-THz (e.g., 120 GHz) frequencies are being investigated for the large available spectrum. Communication at these frequencies requires beam-type connectivity as a solution for the severe path loss attenuation. However, beams can be blocked, with negative consequences for communication reliability. Blockage prediction is necessary and challenging when the blocker is dynamic in high mobility scenarios such as Vehicle-to-Vehicle (V2V). This paper presents an analytical model to derive the unconditional probability of blockage in a highway multi-lane scenario. The proposed model accounts for the traffic density, the 3D dimensions of the vehicles, and the position of the antennas. Moreover, by setting the communication parameters and a target quality of service, it is possible to predict the signal-to-noise ratio distribution and the service probability, which can be used for resource scheduling. Exhaustive numerical results confirm the validity of the proposed model.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Oct 2021.
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Citing papers per year: 2024: 1; 2025: 3; 2026: 1.
5 papers.
- 2025World Model-Based Learning for Long-Term Age of Information Minimization in Vehicular Networkscited by 5
- 2025Dual-Mind World Models: A General Framework for Learning in Dynamic Wireless Networkscited by 4
- 2025Multi-Modal Sensor Fusion for Proactive Blockage Prediction in mmWave Vehicular Networkscited by 1
- 2024Deep Learning-based Target-To-User Association in Integrated Sensing and Communication Systemscited by 0
- 2026Enwar 3.0: An Agentic Multi-Modal LLM Orchestrator for Situation-Aware Beamforming, Blockage Prediction, and Handover Managementcited by 0
- 2026Enwar 3.0: An Agentic Multi-Modal LLM Orchestrator for Situation-Aware Beamforming, Blockage Prediction, and Handover Management4 May 2026
- 2025Dual-Mind World Models: A General Framework for Learning in Dynamic Wireless Networks28 Oct 2025
- 2025Multi-Modal Sensor Fusion for Proactive Blockage Prediction in mmWave Vehicular Networks21 Jul 2025
- 2025World Model-Based Learning for Long-Term Age of Information Minimization in Vehicular Networks3 May 2025
- 2024Deep Learning-based Target-To-User Association in Integrated Sensing and Communication Systems11 Jan 2024