The $\kappa$-$\mu$ Shadowed Fading Model: Unifying the $\kappa$-$\mu$ and $\eta$-$\mu$ Distributions
Laureano Moreno-Pozas, F. Javier Lopez-Martinez, Jos\'e F. Paris and, Eduardo Martos-Naya

TL;DR
This paper introduces the $ppa$-$mu$ shadowed fading model, unifying $ppa$-$mu$ and $ta$-$mu$ models, and derives closed-form asymptotic capacity expressions to analyze system performance under these fading conditions.
Contribution
It unifies two popular fading models into a more general, tractable model and provides new closed-form expressions for asymptotic ergodic capacity.
Findings
The $ppa$-$mu$ shadowed model includes $ta$-$mu$ as a special case.
Closed-form asymptotic capacity expressions are derived for the unified model.
The effects of fading parameters on system performance are illustrated.
Abstract
This paper shows that the recently proposed - shadowed fading model includes, besides the - model, the - fading model as a particular case. This has important relevance in practice, as it allows for the unification of these popular fading distributions through a more general, yet equally tractable, model. The convenience of new underlying physical models is discussed. Then, we derive simple and novel closed-form expressions for the asymptotic ergodic capacity in - shadowed fading channels, which illustrate the effects of the different fading parameters on the system performance. By exploiting the unification here unveiled, the asymptotic capacity expressions for the - and - fading models are also obtained in closed-form as special cases.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
