3,966 research outputs found
Enhanced Trellis Coded Multiple Access (ETCMA)
We propose an enhanced version of trellis coded multiple access (TCMA), an
overloaded multiple access scheme that outperforms the original TCMA in terms
of achieved spectral efficiency. Enhanced TCMA (ETCMA) performs simultaneous
transmission of multiple data streams intended for users experiencing similar
signal-to-noise ratios and can be employed both in the uplink and in the
downlink of wireless systems, thus overcoming one of the main limitations of
TCMA. Thanks to a new receiver algorithm, ETCMA is capable of delivering a
significantly higher spectral efficiency. We show that ETCMA approaches the
capacity of the Additive White Gaussian Noise channel for a wide range of
signal-to-noise ratios.Comment: 5 pages, 5 figure
Effect of Local Magnetic Moments on the Metallic Behavior in Two Dimensions
The temperature dependence of conductivity in the metallic phase
of a two-dimensional electron system in silicon has been studied for different
concentrations of local magnetic moments. The local moments have been induced
by disorder, and their number was varied using substrate bias. The data suggest
that in the limit of the metallic behavior, as characterized by
, is suppressed by an arbitrarily small amount of scattering by
local magnetic moments.Comment: 4 pages, revtex, plus four encapsulated postscript figure
Central limit theorems and diffusion approximations for multiscale Markov chain models
Ordinary differential equations obtained as limits of Markov processes appear
in many settings. They may arise by scaling large systems, or by averaging
rapidly fluctuating systems, or in systems involving multiple time-scales, by a
combination of the two. Motivated by models with multiple time-scales arising
in systems biology, we present a general approach to proving a central limit
theorem capturing the fluctuations of the original model around the
deterministic limit. The central limit theorem provides a method for deriving
an appropriate diffusion (Langevin) approximation.Comment: Published in at http://dx.doi.org/10.1214/13-AAP934 the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Asymptotic analysis of multiscale approximations to reaction networks
A reaction network is a chemical system involving multiple reactions and
chemical species. Stochastic models of such networks treat the system as a
continuous time Markov chain on the number of molecules of each species with
reactions as possible transitions of the chain. In many cases of biological
interest some of the chemical species in the network are present in much
greater abundance than others and reaction rate constants can vary over several
orders of magnitude. We consider approaches to approximation of such models
that take the multiscale nature of the system into account. Our primary example
is a model of a cell's viral infection for which we apply a combination of
averaging and law of large number arguments to show that the ``slow'' component
of the model can be approximated by a deterministic equation and to
characterize the asymptotic distribution of the ``fast'' components. The main
goal is to illustrate techniques that can be used to reduce the dimensionality
of much more complex models.Comment: Published at http://dx.doi.org/10.1214/105051606000000420 in the
Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute
of Mathematical Statistics (http://www.imstat.org
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