SINR and Throughput of Dense Cellular Networks with Stretched Exponential Path Loss

TL;DR

This paper introduces a stretched exponential path loss model for dense cellular networks, derives key performance metrics, and analyzes how network density impacts coverage, throughput, and spectral efficiency.

Contribution

It proposes a new short-range path loss model and integrates it into stochastic geometry analysis for dense networks, providing novel insights into network performance.

Findings

Coverage probability expressions derived for the new model.

Potential throughput maximized at an optimal BS density.

Area spectral efficiency increases with BS density and converges at high densities.

Abstract

Distance-based attenuation is a critical aspect of wireless communications. As opposed to the ubiquitous power-law path loss model, this paper proposes a stretched exponential path loss model that is suitable for short-range communication. In this model, the signal power attenuates over a distance $r$ as $e^{-\alpha r^{\beta}}$, where $\alpha,\beta$ are tunable parameters. Using experimental propagation measurements, we show that the proposed model is accurate for short to moderate distances in the range $r \in (5,300)$ meters and so is a suitable model for dense and ultradense networks. We integrate this path loss model into a downlink cellular network with base stations modeled by a Poisson point process, and derive expressions for the coverage probability, potential throughput, and area spectral efficiency. Although the most general result for coverage probability has a double integral, several special cases are given where the coverage probability has a compact or even closed form. We then show that the potential throughput is maximized for a particular BS density and then collapses to zero for high densities, assuming a fixed SINR threshold. We next prove that the area spectral efficiency, which assumes an adaptive SINR threshold, is non-decreasing with the BS density and converges to a constant for high densities.