Closed-Form Path-Loss Predictor for Gaussianly Distributed Nodes

TL;DR

This paper derives a closed-form expression for path-loss distribution between a base station and Gaussian-distributed nodes, eliminating the need for Monte Carlo simulations in network analysis.

Contribution

It introduces a novel, exact closed-form formula for path-loss distribution in networks with Gaussian node placement, improving efficiency over traditional simulation methods.

Findings

The closed-form expression accurately predicts path-loss distribution.

Simulation results validate the theoretical model.

Applicable to IEEE 802.20 standard scenarios.

Abstract

The emulation of wireless nodes spatial position is a practice used by deployment engineers and network planners to analyze the characteristics of a network. In particular, nodes geolocation will directly impact factors such as connectivity, signals fidelity, and service quality. In literature, in addition to typical homogenous scattering, normal distribution is frequently used to model mobiles concentration in a cellular system. Moreover, Gaussian dropping is often considered as an effective placement method for airborne sensor deployment. Despite the practicality of this model, getting the network channel loss distribution still relies on exhaustive Monte Carlo simulation. In this paper, we argue the need for this inefficient approach and hence derived a generic and exact closed-form expression for the path-loss distribution density between a base-station and a network of nodes. Simulation was used to reaffirm the validity of the theoretical analysis using values from the new IEEE 802.20 standard.