# Coverage and Rate Analysis for Millimeter Wave Cellular Networks

arXiv: https://arxiv.org/abs/1402.6430 · HTML: https://mlsanity.com/paper/1402.6430/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2014.

## Built on by (most cited first, 9 of 9)

- [On the Convergence Time of Federated Learning Over Wireless Networks Under Imperfect CSI](https://mlsanity.com/paper/2104.00331/) (2021)
- [Deep Learning Assisted Calibrated Beam Training for Millimeter-Wave Communication Systems](https://mlsanity.com/paper/2101.05206/) (2021)
- [A Reinforcement Learning Approach for Scheduling in mmWave Networks](https://mlsanity.com/paper/2108.00548/) (2021)
- [Transferable and Distributed User Association Policies for 5G and Beyond Networks](https://mlsanity.com/paper/2106.02540/) (2021)
- [Fast Change Identification in Multi-Play Bandits and its Applications in Wireless Networks](https://mlsanity.com/paper/2205.10366/) (2022)
- [Enhancing Reliability in Federated mmWave Networks: A Practical and Scalable Solution using Radar-Aided Dynamic Blockage Recognition](https://mlsanity.com/paper/2307.06834/) (2023)
- [On Single-User Interactive Beam Alignment in Next Generation Systems: A Deep Learning Viewpoint](https://mlsanity.com/paper/2102.10229/) (2021)
- [Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM)](https://mlsanity.com/paper/2201.00626/) (2021)
- [CoBA: Integrated Deep Learning Model for Reliable Low-Altitude UAV Classification in mmWave Radio Networks](https://mlsanity.com/paper/2601.20605/) (2026)

## Abstract (arXiv)

Millimeter wave (mmWave) holds promise as a carrier frequency for fifth generation cellular networks. Because mmWave signals are sensitive to blockage, prior models for cellular networks operated in the ultra high frequency (UHF) band do not apply to analyze mmWave cellular networks directly. Leveraging concepts from stochastic geometry, this paper proposes a general framework to evaluate the coverage and rate performance in mmWave cellular networks. Using a distance-dependent line-of-site (LOS) probability function, the locations of the LOS and non-LOS base stations are modeled as two independent non-homogeneous Poisson point processes, to which different path loss laws are applied. Based on the proposed framework, expressions for the signal-to-noise-and-interference ratio (SINR) and rate coverage probability are derived. The mmWave coverage and rate performance are examined as a function of the antenna geometry and base station density. The case of dense networks is further analyzed by applying a simplified system model, in which the LOS region of a user is approximated as a fixed LOS ball. The results show that dense mmWave networks can achieve comparable coverage and much higher data rates than conventional UHF cellular systems, despite the presence of blockages. The results suggest that the cell size to achieve the optimal SINR scales with the average size of the area that is LOS to a user.