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Proceedings ArticleDOI

SIR asymptotics in general cellular network models

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TLDR
This paper shows that the asymptotics of the SIR distribution near 0 and near infinity can only differ by a constant, and makes a first step towards explaining this remarkable property.
Abstract
It has recently been observed that the SIR distributions of a variety of cellular network models and transmission techniques look very similar in shape. As a result, they are well approximated by a simple horizontal shift of the distribution of the most tractable model, the Poisson point process. This paper makes a first step towards explaining this remarkable property by showing that the asymptotics of the SIR distribution near 0 and near infinity can only differ by a constant.

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Journal ArticleDOI

Asymptotics and Approximation of the SIR Distribution in General Cellular Networks

TL;DR: In this paper, the authors studied the asymptotic gain of the Poisson point process (PPP) for single-tier networks with nearest-base station association and showed that the gain at 0 is determined by the so-called mean interference-to-signal ratio (MISR) between the PPP and the network model under consideration.
Journal ArticleDOI

Approximate SIR Analysis in General Heterogeneous Cellular Networks

TL;DR: Two simple approximative approaches to the SIR distribution of general heterogeneous cellular networks (HCNs) based on the ASAPPP method and the MISR method are proposed, demonstrating that the proposed approaches give a simple yet excellent approximation for the Sir distribution.

Stochastic Geometry-Based Tools for Spatial Modeling and Planning of Future Cellular Networks: Opportunistic Cell Switch-off and Strategic Deployment of Flying Base Stations

Faraj Lagum
TL;DR: This work develops a novel stochastic geometry-based cellular network planning technique that relies on the spatial structure of the network to determine the best deployment or removal locations of the BSs and develops new approaches for mapping between the internal parameters of different point processes commonly used to generate the BS locations.
Journal ArticleDOI

Stronger wireless signals appear more Poisson

TL;DR: New results are derived that show, it is the strongest signals, after being weakened by random propagation effects, that behave like a Poisson process, which supports recent experimental work.
Journal ArticleDOI

Stronger Wireless Signals Appear More Poisson

TL;DR: In this article, it is shown that the strongest signals, after being weakened by random propagation effects, behave like a Poisson process, which supports recent experimental work. But the results are not applicable in practice, since there must be a large number of transmitters with different locations and random propagation effect.
References
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Book

Stochastic Geometry and Its Applications

TL;DR: Random Closed Sets I--The Boolean Model. Random Closed Sets II--The General Case.
Journal ArticleDOI

A Tractable Approach to Coverage and Rate in Cellular Networks

TL;DR: The proposed model is pessimistic (a lower bound on coverage) whereas the grid model is optimistic, and that both are about equally accurate, and the proposed model may better capture the increasingly opportunistic and dense placement of base stations in future networks.
Book

Real Analysis: Modern Techniques and Their Applications

TL;DR: This book covers the subject matter that is central to mathematical analysis: measure and integration theory, some point set topology, and rudiments of functional analysis.
Book

Stochastic Geometry for Wireless Networks

TL;DR: This rigorous introduction to stochastic geometry will enable you to obtain powerful, general estimates and bounds of wireless network performance and make good design choices for future wireless architectures and protocols that efficiently manage interference effects.
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