Fluctuating Line-of-Sight Fading Distribution: Statistical Characterization and Applications

TL;DR

This paper introduces the fluctuating Line-of-Sight (fLoS) fading model, a flexible statistical framework that generalizes many known fading distributions and is useful for analyzing wireless channels affected by aging.

Contribution

The paper develops the fLoS fading model using hypergeometric functions, provides a numerical computation method, and demonstrates its application to wireless system analysis.

Findings

fLoS model encompasses Nakagami-m, Hoyt, Rice, and Rician shadowed distributions.

Finite mixture of κ-μ distributions for integer k.

Accurate approximation of outage probability and ergodic capacity.

Abstract

We introduce the fluctuating Line-of-Sight (fLoS) fading model, characterized by parameters $K$, $k$, $\lambda$, and $\Omega$. The fLoS fading distribution is expressed in terms of the multivariate confluent hypergeometric functions $\Psi_2$, $\Phi_3^{(n)}$, and $\Phi_3 = \Phi_3^{(2)}$ and encompasses well-known distributions, such as the Nakagami-$m$, Hoyt, Rice, and Rician shadowed fading distributions as special cases. An efficient method to numerically compute the fLoS fading distribution is also addressed. Notably, for a positive integer $k$, the fLoS fading distribution simplifies to a finite mixture of $\kappa$-$\mu$ distributions. Additionally, we analyze the outage probability and Ergodic capacity, presenting a tailored Prony's approximation method for the latter. Numerical results are presented to show the impact of the fading parameters and verify the accuracy of the proposed approximation. Moreover, we illustrate an application of the proposed fLoS fading distribution for characterizing wireless systems affected by channel aging.