1,435 research outputs found
Information capacity in the weak-signal approximation
We derive an approximate expression for mutual information in a broad class
of discrete-time stationary channels with continuous input, under the
constraint of vanishing input amplitude or power. The approximation describes
the input by its covariance matrix, while the channel properties are described
by the Fisher information matrix. This separation of input and channel
properties allows us to analyze the optimality conditions in a convenient way.
We show that input correlations in memoryless channels do not affect channel
capacity since their effect decreases fast with vanishing input amplitude or
power. On the other hand, for channels with memory, properly matching the input
covariances to the dependence structure of the noise may lead to almost
noiseless information transfer, even for intermediate values of the noise
correlations. Since many model systems described in mathematical neuroscience
and biophysics operate in the high noise regime and weak-signal conditions, we
believe, that the described results are of potential interest also to
researchers in these areas.Comment: 11 pages, 4 figures; accepted for publication in Physical Review
Efficient LLR Calculation for Non-Binary Modulations over Fading Channels
Log-likelihood ratio (LLR) computation for non-binary modulations over fading
channels is complicated. A measure of LLR accuracy on asymmetric binary
channels is introduced to facilitate good LLR approximations for non-binary
modulations. Considering piecewise linear LLR approximations, we prove
convexity of optimizing the coefficients according to this measure. For the
optimized approximate LLRs, we report negligible performance losses compared to
true LLRs.Comment: Submitted to IEEE Transactions on Communication
Asymptotic Estimates in Information Theory with Non-Vanishing Error Probabilities
This monograph presents a unified treatment of single- and multi-user
problems in Shannon's information theory where we depart from the requirement
that the error probability decays asymptotically in the blocklength. Instead,
the error probabilities for various problems are bounded above by a
non-vanishing constant and the spotlight is shone on achievable coding rates as
functions of the growing blocklengths. This represents the study of asymptotic
estimates with non-vanishing error probabilities.
In Part I, after reviewing the fundamentals of information theory, we discuss
Strassen's seminal result for binary hypothesis testing where the type-I error
probability is non-vanishing and the rate of decay of the type-II error
probability with growing number of independent observations is characterized.
In Part II, we use this basic hypothesis testing result to develop second- and
sometimes, even third-order asymptotic expansions for point-to-point
communication. Finally in Part III, we consider network information theory
problems for which the second-order asymptotics are known. These problems
include some classes of channels with random state, the multiple-encoder
distributed lossless source coding (Slepian-Wolf) problem and special cases of
the Gaussian interference and multiple-access channels. Finally, we discuss
avenues for further research.Comment: Further comments welcom
A Simple Derivation of the Refined Sphere Packing Bound Under Certain Symmetry Hypotheses
A judicious application of the Berry-Esseen theorem via suitable Augustin
information measures is demonstrated to be sufficient for deriving the sphere
packing bound with a prefactor that is
for all codes on certain
families of channels -- including the Gaussian channels and the non-stationary
Renyi symmetric channels -- and for the constant composition codes on
stationary memoryless channels. The resulting non-asymptotic bounds have
definite approximation error terms. As a preliminary result that might be of
interest on its own, the trade-off between type I and type II error
probabilities in the hypothesis testing problem with (possibly non-stationary)
independent samples is determined up to some multiplicative constants, assuming
that the probabilities of both types of error are decaying exponentially with
the number of samples, using the Berry-Esseen theorem.Comment: 20 page
Coding in the Finite-Blocklength Regime: Bounds based on Laplace Integrals and their Asymptotic Approximations
In this paper we provide new compact integral expressions and associated
simple asymptotic approximations for converse and achievability bounds in the
finite blocklength regime. The chosen converse and random coding union bounds
were taken from the recent work of Polyanskyi-Poor-Verdu, and are investigated
under parallel AWGN channels, the AWGN channels, the BI-AWGN channel, and the
BSC. The technique we use, which is a generalization of some recent results
available from the literature, is to map the probabilities of interest into a
Laplace integral, and then solve (or approximate) the integral by use of a
steepest descent technique. The proposed results are particularly useful for
short packet lengths, where the normal approximation may provide unreliable
results.Comment: 29 pages, 10 figures. Submitted to IEEE Trans. on Information Theory.
Matlab code available from http://dgt.dei.unipd.it section Download->Finite
Blocklength Regim
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