Outage probability of macrodiversity reception in the presence of Gamma long-term fading, Rayleigh short-term fading and Rician co-channel interference

Krstić, Dragana; Suljović, Suad; Milić, Dejan; Panic, Stefan; Stefanović, Mihajlo

doi:10.1007/s12243-017-0593-4

Cited by 11 publications

(9 citation statements)

References 15 publications

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“…Additionally, it can be seen that, even in cases of the same model size, the processing speed might increase due to more complex fading and cointerference distributions considered. Despite the fact that additional time is needed for model-to-model transformation (which is also included for the allocation step during the comparison), this step is still obviously faster compared to our previous works in single-objective mode [57,58]. When it comes to the execution time of multiobjective mode, it is in line with the results achieved by our previous works relying on single-objective linear optimization using AMPL.…”

Section: Discussionsupporting

confidence: 82%

“…e performance analysis of selection diversity used at the reception under simultaneous occurrence of multipath fading and shadowing was done, through derived novel composite Gamma long-time faded α− η− μ fading distribution. Macrodiversity system with SC in the receiver and two microdiversity SC combiners, under the influence of Rayleigh short-term fading, Rician CCI, and Gamma shadowing, was considered in [57].…”

Section: Mitigation Of the Impact Of Fadingmentioning

confidence: 99%

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

Krstić

Petrović

Al-Azzoni

2022

Mathematical Problems in Engineering

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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.

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Section: Discussionsupporting

confidence: 82%

Section: Mitigation Of the Impact Of Fadingmentioning

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

Self Cite

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“…Again, we point out that BX is a general distribution, and the results obtained here can be compared with the results of earlier published papers [23][24][25] by setting the particular parameters values defined above. The obtained result in ( 15) is reduced to special cases of existing fading channels shown in earlier works.…”

Section: The Outage Probability Of the Output Sirsupporting

confidence: 51%

“…The following published case in [25] can be obtained by replacing the CCI parameters to be κ y = K and µ = 1, when expression (15) will become expression (7) from [25], i.e., we obtain the Pout for the channel disturbed by BX fading and the Rician CCI which was published in [23]. Also, the case analyzed in [23,25] for Pout in the presence of Rician fading and Nakagami-m CCI will be obtained from (15) by setting the parameter m to be 1, κ y to be 0, and µ = m.…”

Section: The Outage Probability Of the Output Sirmentioning

confidence: 99%

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MDE and LLM Synergy for Network Experimentation: Case Analysis of Wireless System Performance in Beaulieu-Xie Fading and κ-µ Co-Channel Interference Environment with Diversity Combining

Krstic,

Suljovic,

Djordjevic

et al. 2024

Sensors

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Channel modeling is a first step towards the successful projecting of any wireless communication system. Hence, in this paper, we analyze the performance at the output of a multi-branch selection combining (SC) diversity receiver in a wireless environment that has been distracted by fading and co-channel interference (CCI), whereby the fading is modelled by newer Beaulieu-Xie (BX) distribution, and the CCI is modelled by the κ-µ distribution. The BX distribution provides the ability to include in consideration any number of line-of-sight (LOS) useful signal components and non-LOS (NLOS) useful signal components. This distribution contains characteristics of some other fading models thanks to its flexible fading parameters, which also applies to the κ-µ distribution. We derived here the expressions for the probability density function (PDF) and cumulative distribution function (CDF) for the output signal-to-co-channel interference ratio (SIR). After that, other performances are obtained, namely: outage probability (Pout), channel capacity (CC), moment-generating function (MGF), average bit error probability (ABEP), level crossing rate (LCR), and average fade duration (AFD). Numerical results are presented in several graphs versus the SIR for different values of fading and CCI parameters, as well as the number of input branches in the SC receiver. Then, the impact of parameters on all performance is checked. From our numerical results, it is possible to directly obtain the performance for all derived and displayed quantities for cases of previously known distributions of fading and CCI by inserting the appropriate parameter values. In the second part of the paper, a workflow for automated network experimentation relying on the synergy of Large Language Models (LLMs) and model-driven engineering (MDE) is presented, while the previously derived expressions are used for evaluation. Due to the aforementioned, the biggest value of the obtained results is the applicability to the cases of a large number of other distributions for fading and CCI by replacing the corresponding parameters in the formulas for the respective performances.

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Generalized carrier to interference ratio analysis for shotgun cellular systems in multiple dimensions over composite Rayleigh–Lognormal (Suzuki) fading

Khodadoust

2018

Telecommun Syst

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Outage probability of macrodiversity reception in the presence of Gamma long-term fading, Rayleigh short-term fading and Rician co-channel interference

Cited by 11 publications

References 15 publications

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

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

MDE and LLM Synergy for Network Experimentation: Case Analysis of Wireless System Performance in Beaulieu-Xie Fading and κ-µ Co-Channel Interference Environment with Diversity Combining

Generalized carrier to interference ratio analysis for shotgun cellular systems in multiple dimensions over composite Rayleigh–Lognormal (Suzuki) fading

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