17,998 research outputs found
Large-scale Spatial Distribution Identification of Base Stations in Cellular Networks
The performance of cellular system significantly depends on its network
topology, where the spatial deployment of base stations (BSs) plays a key role
in the downlink scenario. Moreover, cellular networks are undergoing a
heterogeneous evolution, which introduces unplanned deployment of smaller BSs,
thus complicating the performance evaluation even further. In this paper, based
on large amount of real BS locations data, we present a comprehensive analysis
on the spatial modeling of cellular network structure. Unlike the related
works, we divide the BSs into different subsets according to geographical
factor (e.g. urban or rural) and functional type (e.g. macrocells or
microcells), and perform detailed spatial analysis to each subset. After
examining the accuracy of Poisson point process (PPP) in BS locations modeling,
we take into account the Gibbs point processes as well as Neyman-Scott point
processes and compare their accuracy in view of large-scale modeling test.
Finally, we declare the inaccuracy of the PPP model, and reveal the general
clustering nature of BSs deployment, which distinctly violates the traditional
assumption. This paper carries out a first large-scale identification regarding
available literatures, and provides more realistic and more general results to
contribute to the performance analysis for the forthcoming heterogeneous
cellular networks
Two-tier Spatial Modeling of Base Stations in Cellular Networks
Poisson Point Process (PPP) has been widely adopted as an efficient model for
the spatial distribution of base stations (BSs) in cellular networks. However,
real BSs deployment are rarely completely random, due to environmental impact
on actual site planning. Particularly, for multi-tier heterogeneous cellular
networks, operators have to place different BSs according to local coverage and
capacity requirement, and the diversity of BSs' functions may result in
different spatial patterns on each networking tier. In this paper, we consider
a two-tier scenario that consists of macrocell and microcell BSs in cellular
networks. By analyzing these two tiers separately and applying both classical
statistics and network performance as evaluation metrics, we obtain accurate
spatial model of BSs deployment for each tier. Basically, we verify the
inaccuracy of using PPP in BS locations modeling for either macrocells or
microcells. Specifically, we find that the first tier with macrocell BSs is
dispersed and can be precisely modelled by Strauss point process, while Matern
cluster process captures the second tier's aggregation nature very well. These
statistical models coincide with the inherent properties of macrocell and
microcell BSs respectively, thus providing a new perspective in understanding
the relationship between spatial structure and operational functions of BSs
Stochastic Geometry Modeling of Cellular Networks: Analysis, Simulation and Experimental Validation
Due to the increasing heterogeneity and deployment density of emerging
cellular networks, new flexible and scalable approaches for their modeling,
simulation, analysis and optimization are needed. Recently, a new approach has
been proposed: it is based on the theory of point processes and it leverages
tools from stochastic geometry for tractable system-level modeling, performance
evaluation and optimization. In this paper, we investigate the accuracy of this
emerging abstraction for modeling cellular networks, by explicitly taking
realistic base station locations, building footprints, spatial blockages and
antenna radiation patterns into account. More specifically, the base station
locations and the building footprints are taken from two publicly available
databases from the United Kingdom. Our study confirms that the abstraction
model based on stochastic geometry is capable of accurately modeling the
communication performance of cellular networks in dense urban environments.Comment: submitted for publicatio
Wireless networks appear Poissonian due to strong shadowing
Geographic locations of cellular base stations sometimes can be well fitted
with spatial homogeneous Poisson point processes. In this paper we make a
complementary observation: In the presence of the log-normal shadowing of
sufficiently high variance, the statistics of the propagation loss of a single
user with respect to different network stations are invariant with respect to
their geographic positioning, whether regular or not, for a wide class of
empirically homogeneous networks. Even in perfectly hexagonal case they appear
as though they were realized in a Poisson network model, i.e., form an
inhomogeneous Poisson point process on the positive half-line with a power-law
density characterized by the path-loss exponent. At the same time, the
conditional distances to the corresponding base stations, given their observed
propagation losses, become independent and log-normally distributed, which can
be seen as a decoupling between the real and model geometry. The result applies
also to Suzuki (Rayleigh-log-normal) propagation model. We use
Kolmogorov-Smirnov test to empirically study the quality of the Poisson
approximation and use it to build a linear-regression method for the
statistical estimation of the value of the path-loss exponent
A review of wildland fire spread modelling, 1990-present 3: Mathematical analogues and simulation models
In recent years, advances in computational power and spatial data analysis
(GIS, remote sensing, etc) have led to an increase in attempts to model the
spread and behvaiour of wildland fires across the landscape. This series of
review papers endeavours to critically and comprehensively review all types of
surface fire spread models developed since 1990. This paper reviews models of a
simulation or mathematical analogue nature. Most simulation models are
implementations of existing empirical or quasi-empirical models and their
primary function is to convert these generally one dimensional models to two
dimensions and then propagate a fire perimeter across a modelled landscape.
Mathematical analogue models are those that are based on some mathematical
conceit (rather than a physical representation of fire spread) that
coincidentally simulates the spread of fire. Other papers in the series review
models of an physical or quasi-physical nature and empirical or quasi-empirical
nature. Many models are extensions or refinements of models developed before
1990. Where this is the case, these models are also discussed but much less
comprehensively.Comment: 20 pages + 9 pages references + 1 page figures. Submitted to the
International Journal of Wildland Fir
Spatial interactions in agent-based modeling
Agent Based Modeling (ABM) has become a widespread approach to model complex
interactions. In this chapter after briefly summarizing some features of ABM
the different approaches in modeling spatial interactions are discussed.
It is stressed that agents can interact either indirectly through a shared
environment and/or directly with each other. In such an approach, higher-order
variables such as commodity prices, population dynamics or even institutions,
are not exogenously specified but instead are seen as the results of
interactions. It is highlighted in the chapter that the understanding of
patterns emerging from such spatial interaction between agents is a key problem
as much as their description through analytical or simulation means.
The chapter reviews different approaches for modeling agents' behavior,
taking into account either explicit spatial (lattice based) structures or
networks. Some emphasis is placed on recent ABM as applied to the description
of the dynamics of the geographical distribution of economic activities, - out
of equilibrium. The Eurace@Unibi Model, an agent-based macroeconomic model with
spatial structure, is used to illustrate the potential of such an approach for
spatial policy analysis.Comment: 26 pages, 5 figures, 105 references; a chapter prepared for the book
"Complexity and Geographical Economics - Topics and Tools", P. Commendatore,
S.S. Kayam and I. Kubin, Eds. (Springer, in press, 2014
What is the best spatial distribution to model base station density? A deep dive into two european mobile networks
This paper studies the base station (BS) spatial distributions across different scenarios in urban, rural, and coastal zones, based on real BS deployment data sets obtained from two European countries (i.e., Italy and Croatia). Basically, this paper takes into account different representative statistical distributions to characterize the probability density function of the BS spatial density, including Poisson, generalized Pareto, Weibull, lognormal, and \alpha -Stable. Based on a thorough comparison with real data sets, our results clearly assess that the \alpha -Stable distribution is the most accurate one among the other candidates in urban scenarios. This finding is confirmed across different sample area sizes, operators, and cellular technologies (GSM/UMTS/LTE). On the other hand, the lognormal and Weibull distributions tend to fit better the real ones in rural and coastal scenarios. We believe that the results of this paper can be exploited to derive fruitful guidelines for BS deployment in a cellular network design, providing various network performance metrics, such as coverage probability, transmission success probability, throughput, and delay
On Modeling Coverage and Rate of Random Cellular Networks under Generic Channel Fading
In this paper we provide an analytic framework for computing the expected
downlink coverage probability, and the associated data rate of cellular
networks, where base stations are distributed in a random manner. The provided
expressions are in computable integral forms that accommodate generic channel
fading conditions. We develop these expressions by modelling the cellular
interference using stochastic geometry analysis, then we employ them for
comparing the coverage resulting from various channel fading conditions namely
Rayleigh and Rician fading, in addition to the fading-less channel.
Furthermore, we expand the work to accommodate the effects of random frequency
reuse on the cellular coverage and rate. Monte-Carlo simulations are conducted
to validate the theoretical analysis, where the results show a very close
match
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