Higher Order Statistics for the $\alpha - \eta - \kappa - \mu$  Fading Model

Anjos, André Antônio dos; Marins, Tiago Reis Rufino; Souza, Rausley A. A. de; Yacoub, M.D.

doi:10.1109/tap.2018.2819882

Cited by 26 publications

(24 citation statements)

References 19 publications

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“…In this work, generalized expressions are investigated, however, it is worth pointing out that different values of fading parameters may refer to different channel conditions in mmWave band. In particular, when α = 2.2, η = 73, κ = 5.7, µ = 1.01, p = 1.05, q = 1 the distribution resembles to LOS outdoor channel, whereas, when α = 2.545, η = 0.006, κ = 2.5, µ = 1.98, p = 1.5, q = 1.05 the distribution follows non-LOS outdoor channel conditions at 28 GHz [34]. The probability density function (PDF) of envelop of α − η − κ − µ distribution is given as [35]…”

Section: System Modelmentioning

confidence: 88%

“…All the links are considered to follow α − η − κ − µ distribution [34]. In order to model LOS and non-LOS conditions at 28 GHz, this composite fading distribution has shown significant promise [34]. This distribution is defined by six main parameters including α, η, κ, µ, p, q.…”

Section: System Modelmentioning

confidence: 99%

“…This distribution is defined by six main parameters including α, η, κ, µ, p, q. The details of these parameters along with their individual significance can be easily found in [34]. Primarily, these parameters characterize the effect of nonliterary and multipath components of the wireless channel.…”

Section: System Modelmentioning

confidence: 99%

“…where m k can be obtained with the help of α, η, κ, µ, p, q ([35] (16), (33), (34)). Now, using (4) and…”

Section: System Modelmentioning

confidence: 99%

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Interference Analysis for Vehicle-to-Vehicle Communications at 28 GHz

et al. 2020

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High capacity and ultra-reliable vehicular communication are going to be important aspects of beyond 5G communication networks. However, the vehicular communication problem becomes complex at a large scale when vehicles are roaming on the road, while simultaneously communicating with each other. Moreover, at higher frequencies (like 28 GHz), the dynamics of vehicular communication completely shift towards unpredictability and low-reliability. These factors may result in high packet error and a large amount of interference, resulting in regular disruptions in communications. A thorough understanding of performance variations is the key to moving towards the next generation of vehicular networks. With this intent, this article aims to provide a comprehensive interference analysis, wherein the closed-form expressions of packet error probability (PEP) and ergodic capacity are derived. Using the expression of the PEP, diversity analysis is provided which unveils the impact of channel nonlinearities on the performance of interference-constrained vehicular networks. The insights provided here are expected to pave the way for reliable and high capacity vehicular communication networks.

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Section: System Modelmentioning

confidence: 88%

Section: System Modelmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

“…where m k can be obtained with the help of α, η, κ, µ, p, q ([35] (16), (33), (34)). Now, using (4) and…”

Section: System Modelmentioning

confidence: 99%

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Interference Analysis for Vehicle-to-Vehicle Communications at 28 GHz

et al. 2020

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“…In non-stationary channels, as PDF and CDF are time-varying, analysis must be complemented with higher order statistics, following the path to performance measurement or evaluation. For most applications, second-order statistics, such as LCR and AFD (average fade), represent enough statistical metrics to express this non-stationarity [ 5 , 6 ] and non-linearity feature [ 7 ]. We put our motivation point to the fact that representative general channel 5G modeling tools lack in second-order statistics analysis, although it may represent crucial metadata between physical channel parameters (velocity, direction angles, etc.)…”

Section: Introductionmentioning

confidence: 99%

A Symbolic Encapsulation Point as Tool for 5G Wideband Channel Cross-Layer Modeling

Stefanovic

Blagojević

Pokrajac

et al. 2020

Entropy

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Considering that networks based on New Radio (NR) technology are oriented to provide services of desired quality (QoS), it becomes questionable how to model and predict targeted QoS values, especially if the physical channel is dynamically changing. In order to overcome mobility issues, we aim to support the evaluation of second-order statistics of signal, namely level-crossing rate (LCR) and average fade duration (AFD) that is missing in general channel 5G models. Presenting results from our symbolic encapsulation point 5G (SEP5G) additional tool, we fill this gap and motivate further extensions on current general channel 5G. As a matter of contribution, we clearly propose: (i) anadditional tool for encapsulating different mobile 5G modeling approaches; (ii) extended, wideband, LCR, and AFD evaluation for optimal radio resource allocation modeling; and (iii) lower computational complexity and simulation time regarding analytical expression simulations in related scenario-specific 5G channel models. Using our deterministic channel model for selected scenarios and comparing it with stochastic models, we show steps towards higherlevel finite state Markov chain (FSMC) modeling, where mentioned QoS parameters become more feasible, placing symbolic encapsulation at the center of cross-layer design. Furthermore, we generate values within a specified 5G passband, indicating how it can be used for provisioningoptimal radio resource allocation.

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Exact analysis of two‐branch SC receiver over α$$ \alpha $$‐κ$$ \kappa $$‐μ$$ \mu $$ fading

Chatterjee,

Dixit,

Kumar

et al. 2023

Internet Technology Letters

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We analyze the exact and generalized performance of two‐receiving antennas‐aided selection combining (SC) receivers over ‐‐ faded propagation medium. Especially, we have derived exact new expressions for the average bit/symbol error rate (ABER/ASER), and average channel capacity (ACC) for the considered system. The cumulative distribution function‐based method and probability density function‐based method are employed to derive ABER/ASER, and ACC expressions, respectively. Further, asymptotic ABER/ASER expressions are presented to examine diversity order which depends only on and . The change of fading parameters , , and are highlighted in the system's performance. Further, the correctness of all derived expressions is confirmed through simulations.

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Higher Order Statistics for the $\alpha - \eta - \kappa - \mu$ Fading Model

Cited by 26 publications

References 19 publications

Interference Analysis for Vehicle-to-Vehicle Communications at 28 GHz

Interference Analysis for Vehicle-to-Vehicle Communications at 28 GHz

A Symbolic Encapsulation Point as Tool for 5G Wideband Channel Cross-Layer Modeling

Exact analysis of two‐branch SC receiver over α$$ \alpha $$‐κ$$ \kappa $$‐μ$$ \mu $$ fading

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