53,212 research outputs found
Performance analysis of downlink shared channels in a UMTS network
In light of the expected growth in wireless data communications and the commonly anticipated up/downlink asymmetry, we present a performance analysis of downlink data transfer over \textsc{d}ownlink \textsc{s}hared \textsc{ch}annels (\textsc{dsch}s), arguably the most efficient \textsc{umts} transport channel for medium-to-large data transfers. It is our objective to provide qualitative insight in the different aspects that influence the data \textsc{q}uality \textsc{o}f \textsc{s}ervice (\textsc{qos}). As a most principal factor, the data traffic load affects the data \textsc{qos} in two distinct manners: {\em (i)} a heavier data traffic load implies a greater competition for \textsc{dsch} resources and thus longer transfer delays; and {\em (ii)} since each data call served on a \textsc{dsch} must maintain an \textsc{a}ssociated \textsc{d}edicated \textsc{ch}annel (\textsc{a}-\textsc{dch}) for signalling purposes, a heavier data traffic load implies a higher interference level, a higher frame error rate and thus a lower effective aggregate \textsc{dsch} throughput: {\em the greater the demand for service, the smaller the aggregate service capacity.} The latter effect is further amplified in a multicellular scenario, where a \textsc{dsch} experiences additional interference from the \textsc{dsch}s and \textsc{a}-\textsc{dch}s in surrounding cells, causing a further degradation of its effective throughput. Following an insightful two-stage performance evaluation approach, which segregates the interference aspects from the traffic dynamics, a set of numerical experiments is executed in order to demonstrate these effects and obtain qualitative insight in the impact of various system aspects on the data \textsc{qos}
Hierarchical Economic Agents and their Interactions
We present a new type of spin market model, populated by hierarchical agents,
represented as configurations of sites and arcs in an evolving network. We
describe two analytic techniques for investigating the asymptotic behavior of
this model: one based on the spectral theory of Markov chains and another
exploiting contingent submartingales to construct a deterministic cellular
automaton that approximates the stochastic dynamics. Our study of this system
documents a phase transition between a sub-critical and a super-critical regime
based on the values of a coupling constant that modulates the tradeoff between
local majority and global minority forces. In conclusion, we offer a
speculative socioeconomic interpretation of the resulting distributional
properties of the system.Comment: 38 pages, 13 figures, presented at the 2013 WEHIA conference; to
appear in Journal of Economic Interaction and Coordination, to appear in
Journal of Economic Interaction and Coordinatio
Finite-size and correlation-induced effects in Mean-field Dynamics
The brain's activity is characterized by the interaction of a very large
number of neurons that are strongly affected by noise. However, signals often
arise at macroscopic scales integrating the effect of many neurons into a
reliable pattern of activity. In order to study such large neuronal assemblies,
one is often led to derive mean-field limits summarizing the effect of the
interaction of a large number of neurons into an effective signal. Classical
mean-field approaches consider the evolution of a deterministic variable, the
mean activity, thus neglecting the stochastic nature of neural behavior. In
this article, we build upon two recent approaches that include correlations and
higher order moments in mean-field equations, and study how these stochastic
effects influence the solutions of the mean-field equations, both in the limit
of an infinite number of neurons and for large yet finite networks. We
introduce a new model, the infinite model, which arises from both equations by
a rescaling of the variables and, which is invertible for finite-size networks,
and hence, provides equivalent equations to those previously derived models.
The study of this model allows us to understand qualitative behavior of such
large-scale networks. We show that, though the solutions of the deterministic
mean-field equation constitute uncorrelated solutions of the new mean-field
equations, the stability properties of limit cycles are modified by the
presence of correlations, and additional non-trivial behaviors including
periodic orbits appear when there were none in the mean field. The origin of
all these behaviors is then explored in finite-size networks where interesting
mesoscopic scale effects appear. This study leads us to show that the
infinite-size system appears as a singular limit of the network equations, and
for any finite network, the system will differ from the infinite system
Non-Markov stochastic dynamics of real epidemic process of respiratory infections
The study of social networks and especially of the stochastic dynamics of the
diseases spread in human population has recently attracted considerable
attention in statistical physics. In this work we present a new statistical
method of analyzing the spread of epidemic processes of grippe and acute
respiratory track infections (ARTI) by means of the theory of discrete
non-Markov stochastic processes. We use the results of our last theory (Phys.
Rev. E 65, 046107 (2002)) to study statistical memory effects, long - range
correlation and discreteness in real data series, describing the epidemic
dynamics of human ARTI infections and grippe. We have carried out the
comparative analysis of the data of the two infections (grippe and ARTI) in one
of the industrial districts of Kazan, one of the largest cities of Russia. The
experimental data are analyzed by the power spectra of the initial time
correlation function and the memory functions of junior orders, the phase
portraits of the four first dynamic variables, the three first points of the
statistical non-Markov parameter and the locally averaged kinetic and
relaxation parameters. The received results give an opportunity to provide
strict quantitative description of the regular and stochastic components in
epidemic dynamics of social networks taking into account their time
discreteness and effects of statistical memory. They also allow to reveal the
degree of randomness and predictability of the real epidemic process in the
specific social network.Comment: 18 pages, 8figs, 1 table
Simple deterministic dynamical systems with fractal diffusion coefficients
We analyze a simple model of deterministic diffusion. The model consists of a
one-dimensional periodic array of scatterers in which point particles move from
cell to cell as defined by a piecewise linear map. The microscopic chaotic
scattering process of the map can be changed by a control parameter. This
induces a parameter dependence for the macroscopic diffusion coefficient. We
calculate the diffusion coefficent and the largest eigenmodes of the system by
using Markov partitions and by solving the eigenvalue problems of respective
topological transition matrices. For different boundary conditions we find that
the largest eigenmodes of the map match to the ones of the simple
phenomenological diffusion equation. Our main result is that the difffusion
coefficient exhibits a fractal structure by varying the system parameter. To
understand the origin of this fractal structure, we give qualitative and
quantitative arguments. These arguments relate the sequence of oscillations in
the strength of the parameter-dependent diffusion coefficient to the
microscopic coupling of the single scatterers which changes by varying the
control parameter.Comment: 28 pages (revtex), 12 figures (postscript), submitted to Phys. Rev.
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