Stochastic geometry approach towards interference management and control in cognitive radio network: A survey

Okegbile, Samuel D.; Maharaj, B. T.; Alfa, Attahiru Sule

doi:10.1016/j.comcom.2020.12.011

Cited by 16 publications

(12 citation statements)

References 168 publications

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“…In cognitive networks, devices performing special tasks or with a higher energy burden (such as relay nodes) can be made to harvest energy (see [2]- [4] for example) to support transmissions in the secondary network while meeting the primary reliability threshold. Although proper interference management and control is necessary to effectively protect the activities of the primary users (PU) [5], in cognitive RF-EH networks, achieving a highly reliable communication at the secondary network is desirable albeit quite challenging due to the strict quality of service (QoS) constraint of the primary network.…”

Section: Introductionmentioning

confidence: 99%

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Outage Minimization of Energy Harvesting-Based Relay-Assisted Random Underlay Cognitive Radio Networks With Interference Cancellation

et al. 2021

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This paper incorporates interference cancellation in the outage analysis of a wireless energy harvesting cognitive relay network. Based on an interference threshold, primary receivers are assumed to be able to cancel a fraction of the strongest interferers in the primary network which often dominates the total interference. To achieve this, the coefficient of cancellation is adapted into the analysis to reduce the level of interference. The rationale is to improve the successful transmission probability of the primary network by cancelling interference at its receivers. Interestingly, this reduces the overhead incurred by the secondary network to meet up with the primary outage constraint. In this work, the optimal relay selection range is derived and deployed to minimize the secondary outage probability. Analytical results show that this approach can be used to significantly reduce the secondary outage probability which in turn improves the network performance. However, this is at the cost of reducing the energy harvesting success probability of the relays within the secondary network.INDEX TERMS cognitive relay networks, decode and forward, energy harvesting, interference cancellation, optimal relay selection range, outage probability, stochastic geometry.

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Section: Introductionmentioning

confidence: 99%

“…> 2 is a fair assumption in wireless scenarios[37] 5. This assumption may be relaxed but at the cost of complicating the expressions derived without providing much additional insight[36].4 VOLUME 4, 2016…”

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confidence: 99%

Outage Minimization of Energy Harvesting-Based Relay-Assisted Random Underlay Cognitive Radio Networks With Interference Cancellation

et al. 2021

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“…The most common task of SG in wireless communications is to characterize the signal to interference and noise ratio (SINR), which can then be used to calculate many cellular performance metrics, such as outage probability, coverage probability, spatial opportunity, spatial throughput, network throughput, medium access probability and spectral efficiency [Okegbile et al, 2021], in both the downlink (DL) and uplink (UL) directions. 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].…”

Section: Introductionmentioning

confidence: 99%

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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“…The most common function of stochastic geometry in wireless communications is to characterize the signal to interference and noise ratio (SINR), which can be used to calculate many cellular performance metrics, such as outage probability, coverage probability, spatial opportunity, spatial throughput, network throughput, medium access probability and spectral efficiency [4], in both downlink (DL) and uplink (UL) directions. 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].…”

Section: Introductionmentioning

confidence: 99%

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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Stochastic geometry approach towards interference management and control in cognitive radio network: A survey

Cited by 16 publications

References 168 publications

Outage Minimization of Energy Harvesting-Based Relay-Assisted Random Underlay Cognitive Radio Networks With Interference Cancellation

Outage Minimization of Energy Harvesting-Based Relay-Assisted Random Underlay Cognitive Radio Networks With Interference Cancellation

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

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