The analysis of coverage probability, ASE and EE in heterogeneous ultra-dense networks with power control

Liu, Qiaoshou; Zhang, Zhongpei

doi:10.1016/j.dcan.2020.02.002

Cited by 11 publications

(12 citation statements)

References 19 publications

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“…It should be noted, however, that in light of the huge influx of wireless emissions in recent years, the impact of noise now pales in comparison with interference [Liu et al, 2020b]. As such, there is a growing trend (see, for example, [Tang et al, 2020, Liu and Zhang, 2020, Haroon et al, 2021, Kouzayha et al, 2021) to replace SINR by SIR, and we will follow this trend in the present article.…”

Section: Introductionmentioning

confidence: 89%

“…SG cellular models that end up with coverage probability expressions involving the BS density, λ , abound. Examples of such models for DL are [ElSawy et al, 2017, Liu et al, 2020b, Liu and Zhang, 2020, Kouzayha et al, 2021, Bai and Heath, 2015, Chen and Yuan, 2019, Liu et al, 2020a, Ouamri et al, 2020, Lei et al, 2018, Fadouol, 2020, Gao et al, 2019, Ali et al, 2019, Andrews et al, 2016, Kundu et al, 2020, Sadeghabadi et al, 2020, Wang and Gursoy, 2019. On the other hand, examples of such models for DL are [ElSawy et al, 2017, Liu et al, 2020b, Haroon et al, 2021, Herath et al, 2018, Kouzayha et al, 2018, Mariam et al, 2021, Andrews et al, 2016, Jia et al, 2019, Gao et al, 2019, Ali et al, 2019, Kundu et al, 2020, Sadeghabadi et al, 2020, Wang and Gursoy, 2019.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cellular Coverage Probability Is Independent of Base Station Density under Stochastic Geometric Models

2021

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Stochastic geometry (SG) has been extensively used to model cellular communications, under the assumption that the base stations (BS) are deployed as a Poisson point process in the Euclidean plane. This use has spawned a huge number of articles over the past years for different scenarios, culminating in an equally huge number of expressions for the coverage probability in both the uplink (UL) and downink (DL) cellular directions. The problem is that those expressions include the BS density, λ , which we prove irrelevant in this article. We start by developing a SG model for a baseline cellular scenario, then prove that its coverage probability is independent of λ , contrary to popular belief.

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Section: Introductionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Cellular Coverage Probability Is Independent of Base Station Density under Stochastic Geometric Models

2021

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“…It should be noted, however, that in light the huge of influx of wireless emissions in recent years, the impact of noise now pales in comparison with interference [5]. As such, there is a growing modelling trend (see, for example, [6,7,8,9]) to replace SINR by SIR, and we will follow this trend in the present article. In all cases, the most relevant assumption made when stochastic geometry is used in modelling cellular networks is that base stations (BS) are deployed in the Euclidean space as a Poisson Point Process (PPP), although many variant processes, e.g.…”

Section: Introductionmentioning

confidence: 94%

Stochastic geometric modelling and simulation of cellular systems for coverage probability characterization

Nassar,

Taher,

El-Hady

2021

Preprint

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Stochastic geometry (SG) has been successfully used as a modelling tool for cellular networks to characterize the coverage probability in both the downlink (DL) and uplink (UL) systems, under the assumption that the base stations (BS) are deployed as a Poisson point process. In the present article, we extend this use and provide further results for interference limited and Rayleigh fading networks, culminating in a multifaceted contribution. First, we compactly model the two systems at once, allowing parallels to be drawn and contrast to be created. Also, for DL we manage to obtain two closed form expressions for two special cases. Moreover, for UL, notorious for being difficult, we develop a clever approximation that overcomes the difficulty, yielding excellent results. Additionally, we present two efficient Monte Carlo simulation algorithms, designed primarily to validate the models, but can be of great use for SG modelling of communications systems in general. Finally, we prove two theorems at odds with popular belief in cellular communications research. Specifically, we prove that under the SG model, the coverage probability in both DL and UL is independent of BS density. Based on this revelation, a plethora of results in the literature have to be re-examined to rid them of a parameter that has been proven superfluous.

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“…It should be noted, however, that in light the huge of influx of wireless emissions in recent years, the impact of noise now pales in comparison with interference [4]. As such, there is a growing trend (see, for example, [5], [6], [7], [8]) to replace SINR by SIR, and we will follow this trend in the present article.…”

Section: Introductionmentioning

confidence: 96%

“…These models are developed for different scenarios, but have in common the consideration that the coverage probability is dependent on 𝜆. Examples of such models for DL are [2], [4], [6], [8], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23]. On the other hand, examples of such models for DL are [2], [4], [7], [24], [25], [26], [20], [27], [18], [19], [21], [22], [23].…”

Section: Introductionmentioning

confidence: 99%

Cellular coverage probability is independent of base station density under stochastic geometric models

Nassar¹

2021

Preprint

View full text Add to dashboard Cite

Stochastic geometry (SG) has been extensively used to model cellular communications, under the assumption that the base stations (BS) are deployed as a Poisson point process in the Euclidean plane. This has spawned a huge number of articles over the past years for different scenarios, culminating in an equally huge number of expressions for the coverage probability in both the uplink (UL) and downink (DL) directions. The trouble is that those expressions include the BS density, $\lambda$, which we prove irrelevant in this article. We start by developing a SG model for a baseline cellular scenario, then prove that the coverage probability is independent of $\lambda$, contrary to popular belief.

show abstract

The analysis of coverage probability, ASE and EE in heterogeneous ultra-dense networks with power control

Cited by 11 publications

References 19 publications

Cellular Coverage Probability Is Independent of Base Station Density under Stochastic Geometric Models

Cellular Coverage Probability Is Independent of Base Station Density under Stochastic Geometric Models

Stochastic geometric modelling and simulation of cellular systems for coverage probability characterization

Cellular coverage probability is independent of base station density under stochastic geometric models

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