970 research outputs found
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
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
Indexing Metric Spaces for Exact Similarity Search
With the continued digitalization of societal processes, we are seeing an
explosion in available data. This is referred to as big data. In a research
setting, three aspects of the data are often viewed as the main sources of
challenges when attempting to enable value creation from big data: volume,
velocity and variety. Many studies address volume or velocity, while much fewer
studies concern the variety. Metric space is ideal for addressing variety
because it can accommodate any type of data as long as its associated distance
notion satisfies the triangle inequality. To accelerate search in metric space,
a collection of indexing techniques for metric data have been proposed.
However, existing surveys each offers only a narrow coverage, and no
comprehensive empirical study of those techniques exists. We offer a survey of
all the existing metric indexes that can support exact similarity search, by i)
summarizing all the existing partitioning, pruning and validation techniques
used for metric indexes, ii) providing the time and storage complexity analysis
on the index construction, and iii) report on a comprehensive empirical
comparison of their similarity query processing performance. Here, empirical
comparisons are used to evaluate the index performance during search as it is
hard to see the complexity analysis differences on the similarity query
processing and the query performance depends on the pruning and validation
abilities related to the data distribution. This article aims at revealing
different strengths and weaknesses of different indexing techniques in order to
offer guidance on selecting an appropriate indexing technique for a given
setting, and directing the future research for metric indexes
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
Modeling and Calibration of Gaia, Hipparcos, and Tycho-2 astrometric data for the detection of dark companions
© 2024 The Author(s). Published by the American Astronomical Society. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Hidden within the Gaia satellite’s multiple data releases lies a valuable cache of dark companions. To facilitate the efficient and reliable detection of these companions via combined analyses involving the Gaia, Hipparcos, and Tycho-2 catalogs, we introduce an astrometric modeling framework. This method incorporates analytical least-square minimization and nonlinear parameter optimization techniques to a set of common calibration sources across the different space-based astrometric catalogs. This enables us to discern the error inflation, astrometric jitter, differential parallax zero-points, and frame rotation of various catalogs relative to Gaia Data Release 3 (DR3). Our findings yield the most precise Gaia DR2 calibration parameters to date, revealing notable dependencies on magnitude and color. Intriguingly, we identify submilliarcsecond frame rotation between Gaia DR1 and DR3, along with an estimated astrometric jitter of 2.16 mas for the revised Hipparcos catalog. In a thorough comparative analysis with previous studies, we offer recommendations on calibrating and utilizing different catalogs for companion detection. Furthermore, we provide a user-friendly pipeline (https://github.com/ruiyicheng/Download_HIP_Gaia_GOST) for catalog download and bias correction, enhancing accessibility and usability within the scientific community.Peer reviewe
Modeling and Calibration of Gaia, Hipparcos, and Tycho-2 astrometric data for the detection of dark companions
Hidden within the Gaia satellite's multiple data releases lies a valuable
cache of dark companions. To facilitate the efficient and reliable detection of
these companions via combined analyses involving Gaia, Hipparcos, and Tycho-2
catalogs, we introduce an astrometric modeling framework. This method
incorporates analytical least square minimization and nonlinear parameter
optimization techniques to a set of common calibration sources across the
different space-based astrometric catalogues. This enables us to discern the
error inflation, astrometric jitter, differential parallax zero-point, and
frame rotation of various catalogues relative to Gaia DR3. Our findings yield
the most precise Gaia DR2 calibration parameters to date, revealing notable
dependencies on magnitude and color. Intriguingly, we identify sub-mas frame
rotation between Gaia DR1 and DR3, along with an estimated astrometric jitter
of 2.16 mas for the revised Hipparcos catalog. In a thorough comparative
analysis with previous studies, we offer recommendations on calibrating and
utilizing different catalogs for companion detection. Furthermore, we provide a
user-friendly pipeline (https://github.com/ruiyicheng/Download_HIP_Gaia_GOST)
for catalog download and bias correction, enhancing accessibility and usability
within the scientific community.Comment: 28 pages, 8 figures, 2 tables, accepted for publication in the
Astrophysical Journal Supplement Serie
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