Higher Order Statistics in a mmWave Propagation Environment

Anjos, André Antônio dos; Marins, Tiago Reis Rufino; Silva, Carlos Rafael Nogueira da; Peñarrocha, Vicent Miquel Rodrigo; Rubio, Lorenzo; Reig, Juan; Souza, Rausley A. A. de; Yacoub, M.D.

doi:10.1109/access.2019.2930931

Cited by 23 publications

(21 citation statements)

References 29 publications

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“…The findings in [19] reveal that the α-µ, η-µ, and κ-µ fading channel models are satisfactory for modeling short-term fading in the mm-Wave band. Other interesting results for modeling small-scale fading in mm-Wave communications can be found in [20][21][22]. In our work, we focus on two fading channel models to characterize mm-Wave scenarios in 5G networks and beyond.…”

Section: Introductionmentioning

confidence: 99%

Fading Channel Models for mm-Wave Communications

Urquiza-Aguiar

Paredes

2021

Electronics

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A realistic performance assessment of any wireless communication system requires the use of a fading channel model that reflects its main characteristics. The traditional Rayleigh and Nakagami-m models have been (and still are) the basis of most theoretical research on wireless technologies today, even for emerging technologies, such as millimeter-wave communications (mm-Wave). In this article, we show that the fluctuating multiple-ray (FMR) and κ-μ shadowed models had a better fit (i.e., lowest mean square error statistical test) to field measurements in outdoor environments at 28 GHz than the conventional channel models. Therefore, these generalized models are feasible alternatives that can be used as a benchmark when evaluating communication performance in mm-Wave scenarios.

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Section: Introductionmentioning

confidence: 99%

Fading Channel Models for mm-Wave Communications

Urquiza-Aguiar

Paredes

2021

Electronics

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“…The expressions of their first-order statistics, namely PDF, as well as the description of the corresponding parameters can be found in [21]. In the same way, the expressions of their second-order statistics, namely LCR, as well as the description of the corresponding parameters can be found in [22]. In both cases, the formulations for the FN can be easily obtained in a straightforward form from those of κ-µ ones by setting µ = 1/2.…”

Section: A Fading Modelsmentioning

confidence: 96%

“…In order to exemplify the LCR fitting process, Fig. 4 shows 1 As can be seen in [22],ψ(0) is the second derivative with respect to space of the spatial autocorrelation function at zero. Such a parameter arises from the underlying Gaussian process composing the fading models.…”

Section: B Second-order Statisticsmentioning

confidence: 99%

Fading Evaluation in Standardized 5G Millimeter-Wave Band

et al. 2021

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Recent standardization of portions of the millimeter-wave (mm-wave) band for fifthgeneration (5G) operation has called for further research on how short-term fading behaves in that unexplored part of the spectrum. With such a target, this paper reports on a thorough measurement campaign conducted in an indoor environment characterized by rich-multipath scattering, a part of a modern building, with floor and ceiling constructed of reinforced concrete over steel plates with wood and plasterboardpaneled walls. Particularly, measurements have been performed in a variety of scenarios, under line-ofsight (LoS) and non-line-of-sight (nLoS) conditions, for a wide range of frequencies, namely from 25 to 40 GHz -a span of 15 GHz -therefore, including 26, 28 and 39 GHz. First and second order statistics of representative fading models, namely Rayleigh, Rice, Nakagami, folded normal, α-µ, η-µ, κ-µ, and α-η-κ-µ have been investigated. The metrics used in the analysis were the normalized mean square error (NMSE), the Kolmogorov-Smirnov (KS), and the Akaike information criterion (AIC). Additionally, the study of the κ-µ model is advanced, in which new, exact, simple closed-form expressions for probability density function, cumulative distribution function, and level crossing rate are derived for some particular cases, namely for µ = n + 1/2 in which n ∈ N.

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“…Conventional fading distributions are derived from the assumption that the scattering field in wireless communication has homogeneous characteristics [12]. However, this assumption is undoubtedly an approximation, as the networks that characterize heterogeneous environments have spatially correlated characteristics [13]. Different techniques have been successfully developed in stochastic geometry to model the heterogeneous characteristics of next-generation wireless networks.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Mobility Impact on the Performance of Heterogeneous Wireless Networks Over η – μ Fading Channels

Meesa-ard

Pattaramalai

2021

IEEE Access

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The 5G and beyond networks will intrinsically accommodate a wide range of use-case scenarios and expand the limit of legacy mobile systems. The 5G network architecture can handle the seamless operation of various wireless channels in a heterogeneous environment. The η-µ fading model is well-suited for versatile channels as it adapts to different fading behaviors in a broad-range propagation for non-line-of-sight (NLOS) circumstances. This paper evaluates the performance of heterogeneous wireless networks using η-µ fading channel under mobility conditions. We incorporated the random waypoint (RWP) model with η-µ distribution to model the dynamic behavior of non-homogeneous fading. The derivation of expressions for the probability density function (PDF) and cumulative distribution function (CDF) of the received signal power for a mobile network in all three-dimensional topologies is extracted. Consequently, the outage probability (OP) and average bit error rate (ABER) are analyzed to quantify the performance of the mobile system. The effect of co-channel interference (CCI) is investigated based on a desired and interfering signal transmitted in mobile networks. The proposed novel-form can characterize the performance of a mobile user, and the derivation is useful for measuring the effect of noise and interference on the signal. Finally, the novel-form applicability analyzes the impact of mobility incorporated in different fading channels such as Nakagami-m, Nakagami-q (Hoyt), Rayleigh, and one-sided Gaussian distributions.INDEX TERMS Co-channel interference, η-µ distribution, generalized fading model, heterogeneous network, random waypoint mobility.

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Higher Order Statistics in a mmWave Propagation Environment

Cited by 23 publications

References 29 publications

Fading Channel Models for mm-Wave Communications

Fading Channel Models for mm-Wave Communications

Fading Evaluation in Standardized 5G Millimeter-Wave Band

Evaluating the Mobility Impact on the Performance of Heterogeneous Wireless Networks Over η – μ Fading Channels

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