Stochastic Geometric Coverage Analysis in mmWave Cellular Networks with Realistic Channel and Antenna Radiation Models

TL;DR

This paper develops a stochastic geometry-based framework for mmWave cellular network coverage analysis that incorporates realistic channel and antenna models, providing more accurate SINR coverage predictions validated by simulations.

Contribution

It introduces a new SINR coverage analysis method using realistic channel and antenna models, with derived distributions for aligned and misaligned gains validated by simulations.

Findings

Aligned and misaligned gains follow exponential-logarithmically distributions.

Exponential approximations of gains are sufficiently accurate for practical use.

The framework improves the accuracy of coverage predictions in mmWave networks.

Abstract

Millimeter-wave (mmWave) bands will play an important role in 5G wireless systems. The system performance can be assessed by using models from stochastic geometry that cater for the directivity in the desired signal transmissions as well as the interference, and by calculating the signal-to-interference-plus-noise ratio (SINR) coverage. Nonetheless, the correctness of the existing coverage expressions derived through stochastic geometry may be questioned, as it is not clear whether they capture the impact of the detailed mmWave channel and antenna features. In this study, we propose an SINR coverage analysis framework that includes realistic channel model (from NYU) and antenna element radiation patterns (with isotropic/directional radiation). We first introduce two parameters, aligned gain and misaligned gain, associated with the desired signal beam and the interfering signal beam, respectively. We provide the distributions of the aligned and misaligned gains through curve fitting of system-simulation results. The distribution of these gains is used to determine the distribution of the SINR. We compare the obtained analytical SINR coverage with the corresponding SINR coverage calculated via system-level simulations. The results show that both aligned and misaligned gains can be modeled as exponential-logarithmically distributed random variables with the highest accuracy, and can further be approximated as exponentially distributed random variables with reasonable accuracy. These approximations are thus expected to be useful to evaluate the system performance under ultra-reliable and low-latency communication (URLLC) and evolved mobile broadband (eMBB) scenarios, respectively.