The α-η-κ-F Composite Fading Distribution

Badarneh, Osamah S.; Muhaidat, Sami; Costa, Daniel Benevides da

doi:10.1109/lwc.2020.3017169

Cited by 10 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…and q l are the mean values of the in-phase and quadrature phase components of the multipath cluster l. From (11), one can note that the PDF of R can be derived by averaging the PDF of the α − κ − µ fading over the PDF of ϑ and ξ RVs. However, the PDF of the α − κ − µ fading is included the modified Bessel function of the first kind, I υ (.)…”

Section: Statistical Properties Ofmentioning

confidence: 99%

“…conditions were derived in [10]. These models were then unified in a single distribution which is named α − η − κ − F composite fading [11].…”

Section: Introductionmentioning

confidence: 99%

“…Although different statistical properties have been reported in [4]- [11], they are derived in mathematically intractable expressions. This is because they are expressed in terms of either the hypergeometric or the modified Bessel functions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Unified Composite Distribution and Its Applications to Double Shadowed $α-κ-μ$ Fading Channels

Al-Hmood,

Al-Raweshidy

2020

Preprint

View full text Add to dashboard Cite

In this paper, we propose a mixture Gamma shadowed (MGS) distribution as a unified composite model via representing the shadowing by an inverse Nakagami-m. Accordingly, the exact expression and the asymptotic behaviour at high average signal-to-noise ratio (SNR) regime of the fundamental statistics of a MGS distribution are derived first. These statistics are then applied to analyze the performance of the wireless communications systems over double shadowed α − κ − µ fading via providing the outage probability (OP), the average bit error probability (ABEP), the average channel capacity (ACC), and the effective capacity (EC). The average area under the receiver operating characteristics curve (AUC) of energy detection (ED) is also analyzed. The numerical and simulation results are presented to verify the validation of our analysis.

show abstract

Section: Statistical Properties Ofmentioning

confidence: 99%

“…conditions were derived in [10]. These models were then unified in a single distribution which is named α − η − κ − F composite fading [11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Unified Composite Distribution and Its Applications to Double Shadowed $α-κ-μ$ Fading Channels

Al-Hmood,

Al-Raweshidy

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Besides, the wireless fading channels can be described by a significant number of general models, which are gaining in importance more and more. eir flexibility makes them suitable for situations in which none of the traditional distributions give a good fit [14][15][16][17][18][19][20][21][22][23][24][25]. In addition, their convenience is recognized in practical and realistic environments.…”

Section: Introductionmentioning

confidence: 99%

Model-Driven Approach to Fading-Aware Wireless Network Planning Leveraging Multiobjective Optimization and Deep Learning

Krstić

Petrović

Al-Azzoni

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Efficient resource planning is recognized as one of the key enablers making the large-scale deployment of next-generation wireless networks available for mass usage. Modelling, planning, and software simulation tools reduce both the time needed and costs of their tuning and realization. In this paper, we propose a model-driven framework for proactive network planning relying on synergy of deep learning and multiobjective optimization. The predictions about service demand and energy consumption are taken into account. Also, the impact of degradations resulting from fading and cochannel interference (CCI) effects is also considered. The optimization task is treated as a component allocation problem (CAP) aiming to find the best possible base station allocation for the considered smart city locations with respect to performance and service demand constraints. The goal is to maximize Quality of Service (QoS) while keeping the costs and energy consumption as low as possible. The adoption of a model-driven approach in combination with model-to-model transformations and automated code generation does not only reduce the complexity, making experimentation more rapid and convenient at the same time, but also increase the overall reusability and expandability of the planning tool. According to the obtained results, the proposed solution seems to be promising not only due to achieved benefits but also regarding the execution time, which is shorter than that achieved in our previous works, especially for larger distances. Further, we adopt model-based representation of handover strategies within the planning tool, enabling examination of the dynamic behavior of user-created plan, which is not exploited in other similar works. The main contributions of the paper are (1) wireless network planning (WNP) metamodel, a modelling notation for network plans; (2) model-to-model transformation for conversion of WNP to generalized CAP metamodel; (3) prediction problem (PP) metamodel, high-level abstraction for representation of prediction-related regression and classification problems; (4) code generator that creates PyTorch neural network from PP representation; (5) service demand and energy consumption prediction modules performing regression; (6) multiobjective optimization model for base station allocation; (7) Handover Strategy (HS) metamodel used for description of dynamic aspects and adaptability relevant to network planning.

show abstract

“…The composite α-F fading channel was investigated in [8] with applications to the outage probability (OP) and average bit error rate (ABER) of the wireless communications systems. The composite α-κ-F and α-η-F fading distributions were analysed in [9] whereas the authors in [10] suggested the αη-κ-F fading model.…”

Section: Introductionmentioning

confidence: 99%

On the Statistics of the Extended α-η-F Composite Fading Model and Their Applications

Al‐Hmood¹,

Al-Raweshidy²

2022

Preprint

View full text Add to dashboard Cite

<p>In this paper, the extended α-η-F composite fading distribution is proposed. In this model, the propagation environment of the extended η-F composite fading is assumed to be non-linear via usingα-root instead of square-root for the signal envelope. Hence, the fundamental statistics, namely, the probability density function (PDF), cumulative distribution function (CDF), and moment generating function (MGF) are derived. The performance of wireless communications systems undergoing extended α-η-F composite fading is then analysed in terms of the outage probability (OP), outage capacity (OC), and average bit error rate (ABER). To obtain more insights into the impact of α on the systems performance, the asymptotic expressions at high average signal-to-noise (SNR) regime of the OP,OC, and ABER are also given. The numerical results of the proposed model are affirmed via comparing them with the Monte-Carlo simulations as well as some special cases.</p>

show abstract

The α-η-κ-F Composite Fading Distribution

Cited by 10 publications

References 19 publications

Unified Composite Distribution and Its Applications to Double Shadowed $α-κ-μ$ Fading Channels

Unified Composite Distribution and Its Applications to Double Shadowed $α-κ-μ$ Fading Channels

Model-Driven Approach to Fading-Aware Wireless Network Planning Leveraging Multiobjective Optimization and Deep Learning

On the Statistics of the Extended α-η-F Composite Fading Model and Their Applications

Contact Info

Product

Resources

About