How Should One Fit Channel Measurements to Fading Distributions for Performance Analysis?

TL;DR

This paper investigates the best methods for fitting channel measurements to fading models for accurate performance analysis, highlighting the importance of tail-aware metrics over traditional likelihood-based approaches.

Contribution

It demonstrates that supremum-error fitting metrics with tail awareness provide more reliable estimates of system performance than likelihood-based or average-error metrics.

Findings

Likelihood-based metrics can fail to predict outage probabilities.

Tail-aware supremum-error metrics offer more robust performance estimates.

Traditional fitting methods may misrepresent key performance measures.

Abstract

Accurate channel modeling plays a pivotal role in optimizing communication systems, and fitting field measurements to stochastic models is crucial for capturing the key propagation features and to map these to achievable system performances. In this work, we shed light onto what's the most appropriate alternative for channel fitting, when the ultimate goal is performance analysis. Results show that likelihood-based and average-error metrics should be used with caution, since they can largely fail to predict outage probability measures. We show that supremum-error fitting metrics with tail awareness are more robust to estimate both ergodic and outage performance measures, even when they yield a larger average-error fitting.