2,629 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
Automatic frequency assignment for cellular telephones using constraint satisfaction techniques
We study the problem of automatic frequency assignment for cellular telephone
systems. The frequency assignment problem is viewed as the problem
to minimize the unsatisfied soft constraints in a constraint satisfaction problem
(CSP) over a finite domain of frequencies involving co-channel, adjacent
channel, and co-site constraints. The soft constraints are automatically derived
from signal strength prediction data. The CSP is solved using a generalized
graph coloring algorithm. Graph-theoretical results play a crucial
role in making the problem tractable. Performance results from a real-world
frequency assignment problem are presented.
We develop the generalized graph coloring algorithm by stepwise refinement,
starting from DSATUR and augmenting it with local propagation,
constraint lifting, intelligent backtracking, redundancy avoidance, and iterative
deepening
Partially-Distributed Resource Allocation in Small-Cell Networks
We propose a four-stage hierarchical resource allocation scheme for the
downlink of a large-scale small-cell network in the context of orthogonal
frequency-division multiple access (OFDMA). Since interference limits the
capabilities of such networks, resource allocation and interference management
are crucial. However, obtaining the globally optimum resource allocation is
exponentially complex and mathematically intractable. Here, we develop a
partially decentralized algorithm to obtain an effective solution. The three
major advantages of our work are: 1) as opposed to a fixed resource allocation,
we consider load demand at each access point (AP) when allocating spectrum; 2)
to prevent overloaded APs, our scheme is dynamic in the sense that as the users
move from one AP to the other, so do the allocated resources, if necessary, and
such considerations generally result in huge computational complexity, which
brings us to the third advantage: 3) we tackle complexity by introducing a
hierarchical scheme comprising four phases: user association, load estimation,
interference management via graph coloring, and scheduling. We provide
mathematical analysis for the first three steps modeling the user and AP
locations as Poisson point processes. Finally, we provide results of numerical
simulations to illustrate the efficacy of our scheme.Comment: Accepted on May 15, 2014 for publication in the IEEE Transactions on
Wireless Communication
On the Mixing Time of Geographical Threshold Graphs
We study the mixing time of random graphs in the -dimensional toric unit
cube generated by the geographical threshold graph (GTG) model, a
generalization of random geometric graphs (RGG). In a GTG, nodes are
distributed in a Euclidean space, and edges are assigned according to a
threshold function involving the distance between nodes as well as randomly
chosen node weights, drawn from some distribution. The connectivity threshold
for GTGs is comparable to that of RGGs, essentially corresponding to a
connectivity radius of . However, the degree distributions
at this threshold are quite different: in an RGG the degrees are essentially
uniform, while RGGs have heterogeneous degrees that depend upon the weight
distribution. Herein, we study the mixing times of random walks on
-dimensional GTGs near the connectivity threshold for . If the
weight distribution function decays with for an arbitrarily small constant then the mixing time
of GTG is \mixbound. This matches the known mixing bounds for the
-dimensional RGG
Decentralized Constraint Satisfaction
We show that several important resource allocation problems in wireless
networks fit within the common framework of Constraint Satisfaction Problems
(CSPs). Inspired by the requirements of these applications, where variables are
located at distinct network devices that may not be able to communicate but may
interfere, we define natural criteria that a CSP solver must possess in order
to be practical. We term these algorithms decentralized CSP solvers. The best
known CSP solvers were designed for centralized problems and do not meet these
criteria. We introduce a stochastic decentralized CSP solver and prove that it
will find a solution in almost surely finite time, should one exist, also
showing it has many practically desirable properties. We benchmark the
algorithm's performance on a well-studied class of CSPs, random k-SAT,
illustrating that the time the algorithm takes to find a satisfying assignment
is competitive with stochastic centralized solvers on problems with order a
thousand variables despite its decentralized nature. We demonstrate the
solver's practical utility for the problems that motivated its introduction by
using it to find a non-interfering channel allocation for a network formed from
data from downtown Manhattan
BayesX: Analysing Bayesian structured additive regression models
There has been much recent interest in Bayesian inference for generalized additive and related models. The increasing popularity of Bayesian methods for these and other model classes is mainly caused by the introduction of Markov chain Monte Carlo (MCMC) simulation techniques which allow the estimation of very complex and realistic models. This paper describes the capabilities of the public domain software BayesX for estimating complex regression models with structured additive predictor. The program extends the capabilities of existing software for semiparametric regression. Many model classes well known from the literature are special cases of the models supported by BayesX. Examples are Generalized Additive (Mixed) Models, Dynamic Models, Varying Coefficient Models, Geoadditive Models, Geographically Weighted Regression and models for space-time regression. BayesX supports the most common distributions for the response variable. For univariate responses these are Gaussian, Binomial, Poisson, Gamma and negative Binomial. For multicategorical responses, both multinomial logit and probit models for unordered categories of the response as well as cumulative threshold models for ordered categories may be estimated. Moreover, BayesX allows the estimation of complex continuous time survival and hazardrate models
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