Investigating the Gaussian Convergence of the Distribution of the Aggregate Interference Power in Large Wireless Networks

Aljuaid, Muhammad; Yanıkömeroğlu, Halim

doi:10.1109/tvt.2010.2067452

Cited by 63 publications

(45 citation statements)

References 20 publications

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“…Existing stochastic geometry based work either uses the interference Laplace transform to evaluate simple transmission schemes [7], [8], or models the interference using moment matching gamma distribution [9], [10]. Gaussian distribution is another approximation that is considered for the centered and normalized aggregate wireless interference power [11], [12]. The central limit theorem which justifies the Gaussian approximation, however, does not apply when some of the interferers are dominant.…”

Section: A Background and Related Workmentioning

confidence: 99%

“…Also, the Gaussian distribution does not model the interference very well at low density of interferers or when the exclusion region, the region with no interferers, is relatively small, as the cell sizes shrink. The distribution of the interference power at relatively small exclusion regions has a heavy tail which can not be captured by the Gaussian distribution [12]. Here we propose analytical distribution models characterized by only a few parameters, which can be fitted to simple polynomial functions of channel path loss exponent and shadowing variance, and test their fitness against network simulation based on stochastic geometry.…”

Section: A Background and Related Workmentioning

confidence: 99%

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Interference Modeling for Cellular Networks Under Beamforming Transmission

Elkotby¹,

Vu²

2017

IEEE Trans. Wireless Commun.

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We propose analytical models for the interference power distribution in a cellular system employing MIMO beamforming in rich and limited scattering environments, which capture non line-of-sight signal propagation in the microwave and mmWave bands, respectively. Two candidate models are considered:the Inverse Gaussian and the Inverse Weibull, both are two-parameter heavy tail distributions. We further propose a mixture of these two distributions as a model with three parameters. To estimate the parameters of these distributions, three approaches are used: moment matching, individual distribution maximum likelihood estimation (MLE), and mixture distribution MLE with a designed expectation maximization algorithm. We then introduce simple fitted functions for the mixture model parameters as polynomials of the channel path loss exponent and shadowing variance. To measure the goodness of these models, the information-theoretic metric relative entropy is used to capture the distance from the model distribution

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Section: A Background and Related Workmentioning

confidence: 99%

Section: A Background and Related Workmentioning

confidence: 99%

Interference Modeling for Cellular Networks Under Beamforming Transmission

Elkotby¹,

Vu²

2017

IEEE Trans. Wireless Commun.

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“…Comparing this to (16), one observes that the latter includes the relevant constant and thus can be used to estimate P out , unlike the former. Unfortunately, the (1+o(1)) term appears in the exponent and thus creates a significant uncertainty 8 (see also below). The following Corollary eliminates this uncertainty via an upper bound, leaving vanishing multiplicative uncertainty only.…”

Section: Corollarymentioning

confidence: 99%

“…MMSE spatial filter) has been studied in [7]. While in some special cases the aggregate interference distribution of a large wireless network approaches the Gaussian one [8], it is far from being accurate in general.…”

Section: Introductionmentioning

confidence: 99%

Asymptotic Analysis of Interference in Cognitive Radio Networks

Wen

Loyka

Yongaçoğlu

2012

IEEE J. Select. Areas Commun.

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Abstract-The aggregate interference distribution in cognitive radio networks is studied in a rigorous analytical way using the popular Poisson point process model. While a number of results are available for this model of regular (non-cognitive) networks, cognitive ones present an extra level of difficulties for the analysis, mainly due to the exclusion region around the primary receiver, which are typically addressed via various adhoc approximations (e.g. based on the interference cumulants) or via the large-deviation analysis. Unlike the previous studies, we do not use here ad-hoc approximations but rather obtain the asymptotic interference distribution in a systematic and rigorous way, which also has a guaranteed level of accuracy at the distribution tail. This is in contrast to the large deviation analysis, which provides only the (exponential) order of scaling but not the outage probability itself. Unlike the cumulant-based analysis, our approach provides a guaranteed level of accuracy at the distribution tail. Additionally, our analysis provides a number of novel insights. In particular, we demonstrate that there is a critical transition point below which the outage probability decays only polynomially but above which it decays super-exponentially. This provides a solid analytical foundation to the earlier empirical observations in the literature and also reveals what are the typical ways outage events occur in different regimes. The analysis is further extended to include interference cancelation and fading (from a broad class of distributions). The outage probability is shown to scale down exponentially in the number of canceled nearest interferers in the below-critical region and does not change significantly in the above-critical one. The proposed asymptotic expressions are shown to be accurate in the non-asymptotic regimes.

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“…MMSE spatial filter) has also been studied [6]. While in some special cases the aggregate interference distribution of a large wireless network approaches the Gaussian one [7], it is far from being accurate in general.…”

mentioning

confidence: 99%

Asymptotic analysis of outage probability in cognitive radio networks

Wen

Loyka

Yongaçoğlu

2012

2012 IEEE International Conference on Communications (ICC)

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Abstract-The aggregate interference distribution in cognitive radio networks is studied in a rigorous analytical way using the popular Poisson point process model. While a number of results are available for this model of regular (non-cognitive) networks, cognitive ones present an extra level of difficulty for the analysis, mainly due to the exclusion region around the primary receiver, which are typically addressed via various adhoc approximations (e.g. based on the interference cumulants) or via the large-deviation analysis. Unlike the previous studies, here we do not use ad-hoc approximations but rather obtain the asymptotic interference distribution in a systematic and rigorous way. This is in contrast to the large deviation analysis, which provides only the (exponential) order of scaling but not the outage probability itself. Unlike the cumulant-based analysis, our approach provides a guaranteed level of accuracy at the distribution tail. Additionally, our analysis also provides a number of novel insights. In particular, we demonstrate that there is a critical transition point below which the outage probability decays only polynomially but above which it decays exponentially. This provides a solid analytical foundation to the earlier empirical observations in the literature and also reveals how typical outage events occur in different regimes. In addition, the proposed asymptotic expressions are also shown to be accurate in the nonasymptotic regimes.

show abstract

Investigating the Gaussian Convergence of the Distribution of the Aggregate Interference Power in Large Wireless Networks

Cited by 63 publications

References 20 publications

Interference Modeling for Cellular Networks Under Beamforming Transmission

Interference Modeling for Cellular Networks Under Beamforming Transmission

Asymptotic Analysis of Interference in Cognitive Radio Networks

Asymptotic analysis of outage probability in cognitive radio networks

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