5,770 research outputs found
On Modeling Heterogeneous Wireless Networks Using Non-Poisson Point Processes
Future wireless networks are required to support 1000 times higher data rate,
than the current LTE standard. In order to meet the ever increasing demand, it
is inevitable that, future wireless networks will have to develop seamless
interconnection between multiple technologies. A manifestation of this idea is
the collaboration among different types of network tiers such as macro and
small cells, leading to the so-called heterogeneous networks (HetNets).
Researchers have used stochastic geometry to analyze such networks and
understand their real potential. Unsurprisingly, it has been revealed that
interference has a detrimental effect on performance, especially if not modeled
properly. Interference can be correlated in space and/or time, which has been
overlooked in the past. For instance, it is normally assumed that the nodes are
located completely independent of each other and follow a homogeneous Poisson
point process (PPP), which is not necessarily true in real networks since the
node locations are spatially dependent. In addition, the interference
correlation created by correlated stochastic processes has mostly been ignored.
To this end, we take a different approach in modeling the interference where we
use non-PPP, as well as we study the impact of spatial and temporal correlation
on the performance of HetNets. To illustrate the impact of correlation on
performance, we consider three case studies from real-life scenarios.
Specifically, we use massive multiple-input multiple-output (MIMO) to
understand the impact of spatial correlation; we use the random medium access
protocol to examine the temporal correlation; and we use cooperative relay
networks to illustrate the spatial-temporal correlation. We present several
numerical examples through which we demonstrate the impact of various
correlation types on the performance of HetNets.Comment: Submitted to IEEE Communications Magazin
A universal approach to coverage probability and throughput analysis for cellular networks
This paper proposes a novel tractable approach for accurately analyzing both the coverage probability and the achievable throughput of cellular networks. Specifically, we derive a new procedure referred to as the equivalent uniformdensity plane-entity (EUDPE)method for evaluating the other-cell interference. Furthermore, we demonstrate that our EUDPE method provides a universal and effective means to carry out the lower bound analysis of both the coverage probability and the average throughput for various base-station distribution models that can be found in practice, including the stochastic Poisson point process (PPP) model, a uniformly and randomly distributed model, and a deterministic grid-based model. The lower bounds of coverage probability and average throughput calculated by our proposed method agree with the simulated coverage probability and average throughput results and those obtained by the existing PPP-based analysis, if not better. Moreover, based on our new definition of cell edge boundary, we show that the cellular topology with randomly distributed base stations (BSs) only tends toward the Voronoi tessellation when the path-loss exponent is sufficiently high, which reveals the limitation of this popular network topology
Spatial networks with wireless applications
Many networks have nodes located in physical space, with links more common
between closely spaced pairs of nodes. For example, the nodes could be wireless
devices and links communication channels in a wireless mesh network. We
describe recent work involving such networks, considering effects due to the
geometry (convex,non-convex, and fractal), node distribution,
distance-dependent link probability, mobility, directivity and interference.Comment: Review article- an amended version with a new title from the origina
Characterizing Spatial Patterns of Base Stations in Cellular Networks
The topology of base stations (BSs) in cellular networks, serving as a basis
of networking performance analysis, is considered to be obviously distinctive
with the traditional hexagonal grid or square lattice model, thus stimulating a
fundamental rethinking. Recently, stochastic geometry based models, especially
the Poisson point process (PPP), attracts an ever-increasing popularity in
modeling BS deployment of cellular networks due to its merits of tractability
and capability for capturing nonuniformity. In this study, a detailed
comparison between common stochastic models and real BS locations is performed.
Results indicate that the PPP fails to precisely characterize either urban or
rural BS deployment. Furthermore, the topology of real data in both regions are
examined and distinguished by statistical methods according to the point
interaction trends they exhibit. By comparing the corresponding real data with
aggregative point process models as well as repulsive point process models, we
verify that the capacity-centric deployment in urban areas can be modeled by
typical aggregative processes such as the Matern cluster process, while the
coverage-centric deployment in rural areas can be modeled by representativ
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
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