Bayesian inference based resource efficient relay selection scheme for nakagami fading channels

Ghauri, Aizaz; Tayyab, Ali; Siddiqi, Sadia Jabeen; Khan, Imran; Jan, Sadaqat

doi:10.1109/iceet1.2018.8338644

Cited by 1 publication

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“…is paper provides a framework within a computational reach utilising a SMN construction within the fading environment, which could now be implemented by the practitioner when field data justify it. Further, as a potential future point of departure, these scale mixture density representations situated within the fading environment may provide particular advances within Bayesian computing, especially in the case of the Gibbs sampler, where this hierarchical representation may lessen the computational strain within implementation of Bayesian statistical inference (see, for example, [25,26]). Furthermore, the SMN (α − μ) model can be extended and implemented, for example, within the cascaded α − μ fading environment [3].…”

Section: Resultsmentioning

confidence: 99%

An Enriched α − μ Model as Fading Candidate

Ferreira

Bekker

Marques

et al. 2020

Mathematical Problems in Engineering

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This paper introduces an enriched α − μ distribution which may act as fading model with its origins via the scale mixture construction. The distribution’s characteristics are visited and its feasibility as a fading candidate in wireless communications systems is investigated. The analysis of the system reliability and some performance measures of wireless communications systems over this enriched α − μ fading candidate are illustrated. Computable representations of the Laplace transform for this scale mixture construction are also provided. The derived expressions are explored via numerical investigations. Tractable results are computed in terms of the Meijer G-function. This unified scale mixture approach allows access to previously unconsidered underlying models that may yield improved fits to experimental data in practice.

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Section: Resultsmentioning

confidence: 99%