431 research outputs found
Lookahead Strategies for Sequential Monte Carlo
Based on the principles of importance sampling and resampling, sequential
Monte Carlo (SMC) encompasses a large set of powerful techniques dealing with
complex stochastic dynamic systems. Many of these systems possess strong
memory, with which future information can help sharpen the inference about the
current state. By providing theoretical justification of several existing
algorithms and introducing several new ones, we study systematically how to
construct efficient SMC algorithms to take advantage of the "future"
information without creating a substantially high computational burden. The
main idea is to allow for lookahead in the Monte Carlo process so that future
information can be utilized in weighting and generating Monte Carlo samples, or
resampling from samples of the current state.Comment: Published in at http://dx.doi.org/10.1214/12-STS401 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
A joint time-invariant filtering approach to the linear Gaussian relay problem
In this paper, the linear Gaussian relay problem is considered. Under the
linear time-invariant (LTI) model the problem is formulated in the frequency
domain based on the Toeplitz distribution theorem. Under the further assumption
of realizable input spectra, the LTI Gaussian relay problem is converted to a
joint design problem of source and relay filters under two power constraints,
one at the source and the other at the relay, and a practical solution to this
problem is proposed based on the projected subgradient method. Numerical
results show that the proposed method yields a noticeable gain over the
instantaneous amplify-and-forward (AF) scheme in inter-symbol interference
(ISI) channels. Also, the optimality of the AF scheme within the class of
one-tap relay filters is established in flat-fading channels.Comment: 30 pages, 10 figure
Lecture Notes on Network Information Theory
These lecture notes have been converted to a book titled Network Information
Theory published recently by Cambridge University Press. This book provides a
significantly expanded exposition of the material in the lecture notes as well
as problems and bibliographic notes at the end of each chapter. The authors are
currently preparing a set of slides based on the book that will be posted in
the second half of 2012. More information about the book can be found at
http://www.cambridge.org/9781107008731/. The previous (and obsolete) version of
the lecture notes can be found at http://arxiv.org/abs/1001.3404v4/
On Optimal Power Control for Energy Harvesting Communications with Lookahead
Consider the problem of power control for an energy harvesting communication
system, where the transmitter is equipped with a finite-sized rechargeable
battery and is able to look ahead to observe a fixed number of future energy
arrivals. An implicit characterization of the maximum average throughput over
an additive white Gaussian noise channel and the associated optimal power
control policy is provided via the Bellman equation under the assumption that
the energy arrival process is stationary and memoryless. A more explicit
characterization is obtained for the case of Bernoulli energy arrivals by means
of asymptotically tight upper and lower bounds on both the maximum average
throughput and the optimal power control policy. Apart from their pivotal role
in deriving the desired analytical results, such bounds are highly valuable
from a numerical perspective as they can be efficiently computed using convex
optimization solvers.Comment: 25 pages, 5 figure
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