New Rain Rate Statistics for Emerging Regions: Implications for Wireless Backhaul Planning

TL;DR

This paper evaluates and improves rain rate estimation methods for emerging regions, enhancing the reliability of high-capacity wireless links in tropical rain-heavy areas for better broadband deployment.

Contribution

It identifies biases in current rain rate estimation methods and proposes an improved rainfall climatology to enhance prediction accuracy without losing standard evaluation performance.

Findings

Bias and variance issues affect high rain rate predictions.

Improved rainfall climatology reduces prediction errors.

Enhanced estimates support reliable wireless backhaul planning.

Abstract

As demand for broadband service increases in emerging regions, high-capacity wireless links can accelerate and cost-reduce the deployment of new networks (both backhaul and customer site connection). Such links are increasingly common in developed countries, but their reliability in emerging regions is questioned where very heavy tropical rain is present. Here, we investigate the robustness of the standard (ITU-R P.837-6) method for estimating rain rates using an expanded test dataset. We illustrate how bias/variance issues cause problematic predictions at higher rain rates. We confirm (by construction) that an improved rainfall climatology can largely address these prediction issues without compromising standard ITU fit evaluation metrics.