MmWave vehicle-to-infrastructure communication: Analysis of urban microcellular networks
Not on the map. Why?
Abstract (arXiv)
Vehicle-to-infrastructure (V2I) communication may provide high data rates to vehicles via millimeter-wave (mmWave) microcellular networks. This paper uses stochastic geometry to analyze the coverage of urban mmWave microcellular networks. Prior work used a pathloss model with a line-of-sight probability function based on randomly oriented buildings, to determine whether a link was line-of-sight or non-line-of-sight. In this paper, we use a pathloss model inspired by measurements, which uses a Manhattan distance pathloss model and accounts for differences in pathloss exponents and losses when turning corners. In our model, streets are randomly located as a Manhattan Poisson line process (MPLP) and the base stations (BSs) are distributed according to a Poisson point process. Our model is well suited for urban microcellular networks where the BSs are deployed at street level. Based on this new approach, we derive the coverage probability under certain BS association rules to obtain closed-form solutions without much complexity. In addition, we draw two main conclusions from our work. First, non-line-of-sight BSs are not a major benefit for association or source of interference most of the time. Second, there is an ultra-dense regime where deploying active BSs does not enhance coverage.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Feb 2017.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 0; 2025: 1.
3 papers.
- 2021Deep Learning-based Compressive Beam Alignment in mmWave Vehicular Systemscited by 1
- 2022Interpretable AI-based Large-scale 3D Pathloss Prediction Model for enabling Emerging Self-Driving Networkscited by 1
- 2025Vision Aided Channel Prediction for Vehicular Communications: A Case Study of Received Power Prediction Using RGB Imagescited by 1
- 2025Vision Aided Channel Prediction for Vehicular Communications: A Case Study of Received Power Prediction Using RGB Images25 Jan 2025
- 2022Interpretable AI-based Large-scale 3D Pathloss Prediction Model for enabling Emerging Self-Driving Networks30 Jan 2022
- 2021Deep Learning-based Compressive Beam Alignment in mmWave Vehicular Systems27 Feb 2021