438 research outputs found
Simplicial Homology for Future Cellular Networks
Simplicial homology is a tool that provides a mathematical way to compute the
connectivity and the coverage of a cellular network without any node location
information. In this article, we use simplicial homology in order to not only
compute the topology of a cellular network, but also to discover the clusters
of nodes still with no location information. We propose three algorithms for
the management of future cellular networks. The first one is a frequency
auto-planning algorithm for the self-configuration of future cellular networks.
It aims at minimizing the number of planned frequencies while maximizing the
usage of each one. Then, our energy conservation algorithm falls into the
self-optimization feature of future cellular networks. It optimizes the energy
consumption of the cellular network during off-peak hours while taking into
account both coverage and user traffic. Finally, we present and discuss the
performance of a disaster recovery algorithm using determinantal point
processes to patch coverage holes
Computing the -coverage of a wireless network
Coverage is one of the main quality of service of a wirelessnetwork.
-coverage, that is to be covered simultaneously by network nodes, is
synonym of reliability and numerous applicationssuch as multiple site MIMO
features, or handovers. We introduce here anew algorithm for computing the
-coverage of a wirelessnetwork. Our method is based on the observation that
-coverage canbe interpreted as layers of -coverage, or simply
coverage. Weuse simplicial homology to compute the network's topology and
areduction algorithm to indentify the layers of -coverage. Weprovide figures
and simulation results to illustrate our algorithm.Comment: Valuetools 2019, Mar 2019, Palma de Mallorca, Spain. 2019. arXiv
admin note: text overlap with arXiv:1802.0844
Weighted (Co)homology and Weighted Laplacian
In this paper, we generalize the combinatorial Laplace operator of Horak and
Jost by introducing the -weighted coboundary operator induced by a weight
function . Our weight function is a generalization of Dawson's
weighted boundary map. We show that our above-mentioned generalizations include
new cases that are not covered by previous literature. Our definition of
weighted Laplacian for weighted simplicial complexes is also applicable to
weighted/unweighted graphs and digraphs.Comment: 22 page
Simplicial Multivalued Maps and the Witness Complex for Dynamical Analysis of Time Series
Topology based analysis of time-series data from dynamical systems is
powerful: it potentially allows for computer-based proofs of the existence of
various classes of regular and chaotic invariant sets for high-dimensional
dynamics. Standard methods are based on a cubical discretization of the
dynamics and use the time series to construct an outer approximation of the
underlying dynamical system. The resulting multivalued map can be used to
compute the Conley index of isolated invariant sets of cubes. In this paper we
introduce a discretization that uses instead a simplicial complex constructed
from a witness-landmark relationship. The goal is to obtain a natural
discretization that is more tightly connected with the invariant density of the
time series itself. The time-ordering of the data also directly leads to a map
on this simplicial complex that we call the witness map. We obtain conditions
under which this witness map gives an outer approximation of the dynamics, and
thus can be used to compute the Conley index of isolated invariant sets. The
method is illustrated by a simple example using data from the classical H\'enon
map.Comment: laTeX, 9 figures, 32 page
- …