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Journal ArticleDOI

The κ-μ distribution and the η-μ distribution

Michel Daoud Yacoub
- 11 Jun 2007 - 
- Vol. 49, Iss: 1, pp 68-81
TLDR
In this paper, the authors proposed two general fading distributions, the kappa-mu distribution and the eta-mu distributions, for line-of-sight applications, for which fading models are proposed.
Abstract
This paper presents two general fading distributions, the kappa-mu distribution and the eta-mu distribution, for which fading models are proposed. These distributions are fully characterized in terms of measurable physical parameters. The kappa-mu distribution includes the Rice (Nakagami-n), the Nakagami-m, the Rayleigh, and the one-sided Gaussian distributions as special cases. The eta-mu distribution includes the Hoyt (Nakagami-q), the Nakagami-m, the Rayleigh, and the one-sided Gaussian distributions as special cases. Field measurement campaigns were used to validate these distributions. It was observed that their fit to experimental data outperformed that provided by the widely known fading distributions, such as the Rayleigh, Rice, and Nakagami-m. In particular, the kappa-mu distribution is better suited for line-of-sight applications, whereas the eta-mu distribution gives better results for non-line-of-sight applications.

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Citations
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Modeling Interference in Wireless Ad Hoc Networks

TL;DR: The interference models are presented in this survey from the perspective of the radio capture phenomenon, resulting in a unified view of interference models, which may be helpful when selecting the appropriate model for a given purpose.
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A Mixture Gamma Distribution to Model the SNR of Wireless Channels

TL;DR: A mixture gamma (MG) distribution for the signal-to-noise ratio (SNR) of wireless channels is proposed, which is not only a more accurate model for composite fading, but is also a versatile approximation for any fading SNR.
Book

Foundations of MIMO Communication

TL;DR: Understand the fundamentals of wireless and MIMO communication with this accessible and comprehensive text, which provides a sound treatment of the key concepts underpinning contemporary wireless communication and M IMO, all the way to massive MIMo.
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Statistical Characterization of $\kappa{ - }\mu$ Shadowed Fading

TL;DR: It is shown that the sum and maximum distributions of independent but arbitrarily distributed κ - μ shadowed variates can be expressed in closed form and this set of new statistical results is finally applied to modeling and analysis of several wireless communication systems, e.g., the proposed distribution has applications to land mobile satellite (LMS) communications and underwater acoustic communications (UAC).
Journal ArticleDOI

Secrecy Capacity Analysis Over $\kappa $ – $\mu $ Fading Channels: Theory and Applications

TL;DR: Novel analytical solutions for the probability of strictly positive secrecy capacity (SPSC) and a lower bound of secure outage probability (SOP) for independent and non-identically distributed channel coefficients without parameter constraints are obtained.
References
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Journal ArticleDOI

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Journal ArticleDOI

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TL;DR: The character of multipath-induced propagational fading is reviewed, along with the interpretations underlying use of the Rayleigh fading model to describe the process statistics, and the system engineering problems of attempting to provide quantitative estimates of long-term link or network performance that takes into account the longer term channel variabilities.
Proceedings ArticleDOI

The /spl kappa/-/spl mu/ distribution: a general fading distribution

TL;DR: A general fading distribution-the /spl eta/-/spl mu/ distribution-that includes the one-sided Gaussian, the Rayleigh, and, more generally, the Nakagami (1960) distributions as special cases is presented.
Journal ArticleDOI

The Probability Distribution of the Amplitude of a Constant Vector Plus a Rayleigh-Distributed Vector

TL;DR: In this article, the probability distribution of the instantaneous resultant amplitude of the sum of a constant vector and a Rayleigh-distributed vector is discussed. But the authors emphasize that two distributions are required to describe the Rayleigh distribution of a vector: the distribution of its amplitude and its phase, and that these distributions may be used to describe random variables occurring in ionospheric and irregular terrain propagation problems.
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