1,435 research outputs found
First- and Second-Order Hypothesis Testing for Mixed Memoryless Sources with General Mixture
The first- and second-order optimum achievable exponents in the simple
hypothesis testing problem are investigated. The optimum achievable exponent
for type II error probability, under the constraint that the type I error
probability is allowed asymptotically up to epsilon, is called the
epsilon-optimum exponent. In this paper, we first give the second-order
epsilon-exponent in the case where the null hypothesis and the alternative
hypothesis are a mixed memoryless source and a stationary memoryless source,
respectively. We next generalize this setting to the case where the alternative
hypothesis is also a mixed memoryless source. We address the first-order
epsilon-optimum exponent in this setting. In addition, an extension of our
results to more general setting such as the hypothesis testing with mixed
general source and the relationship with the general compound hypothesis
testing problem are also discussed.Comment: 23 page
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
One-shot lossy quantum data compression
We provide a framework for one-shot quantum rate distortion coding, in which
the goal is to determine the minimum number of qubits required to compress
quantum information as a function of the probability that the distortion
incurred upon decompression exceeds some specified level. We obtain a one-shot
characterization of the minimum qubit compression size for an
entanglement-assisted quantum rate-distortion code in terms of the smooth
max-information, a quantity previously employed in the one-shot quantum reverse
Shannon theorem. Next, we show how this characterization converges to the known
expression for the entanglement-assisted quantum rate distortion function for
asymptotically many copies of a memoryless quantum information source. Finally,
we give a tight, finite blocklength characterization for the
entanglement-assisted minimum qubit compression size of a memoryless isotropic
qubit source subject to an average symbol-wise distortion constraint.Comment: 36 page
Second-Order Asymptotics of Visible Mixed Quantum Source Coding via Universal Codes
This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TIT.2016.2571662The simplest example of a quantum information source with memory is a mixed
source which emits signals entirely from one of two memoryless quantum sources
with given a priori probabilities. Considering a mixed source consisting of a
general one-parameter family of memoryless sources, we derive the second order
asymptotic rate for fixed-length visible source coding. Furthermore, we
specialize our main result to a mixed source consisting of two memoryless
sources. Our results provide the first example of second order asymptotics for
a quantum information-processing task employing a resource with memory. For the
case of a classical mixed source (using a finite alphabet), our results reduce
to those obtained by Nomura and Han [IEEE Trans. on Inf. Th. 59.1 (2013), pp.
1-16]. To prove the achievability part of our main result, we introduce
universal quantum source codes achieving second order asymptotic rates. These
are obtained by an extension of Hayashi's construction [IEEE Trans. on Inf. Th.
54.10 (2008), pp. 4619-4637] of their classical counterparts
Smooth Renyi Entropies and the Quantum Information Spectrum
Many of the traditional results in information theory, such as the channel
coding theorem or the source coding theorem, are restricted to scenarios where
the underlying resources are independent and identically distributed (i.i.d.)
over a large number of uses. To overcome this limitation, two different
techniques, the information spectrum method and the smooth entropy framework,
have been developed independently. They are based on new entropy measures,
called spectral entropy rates and smooth entropies, respectively, that
generalize Shannon entropy (in the classical case) and von Neumann entropy (in
the more general quantum case). Here, we show that the two techniques are
closely related. More precisely, the spectral entropy rate can be seen as the
asymptotic limit of the smooth entropy. Our results apply to the quantum
setting and thus include the classical setting as a special case
Properties of Noncommutative Renyi and Augustin Information
The scaled R\'enyi information plays a significant role in evaluating the
performance of information processing tasks by virtue of its connection to the
error exponent analysis. In quantum information theory, there are three
generalizations of the classical R\'enyi divergence---the Petz's, sandwiched,
and log-Euclidean versions, that possess meaningful operational interpretation.
However, these scaled noncommutative R\'enyi informations are much less
explored compared with their classical counterpart, and lacking crucial
properties hinders applications of these quantities to refined performance
analysis. The goal of this paper is thus to analyze fundamental properties of
scaled R\'enyi information from a noncommutative measure-theoretic perspective.
Firstly, we prove the uniform equicontinuity for all three quantum versions of
R\'enyi information, hence it yields the joint continuity of these quantities
in the orders and priors. Secondly, we establish the concavity in the region of
for both Petz's and the sandwiched versions. This completes the
open questions raised by Holevo
[\href{https://ieeexplore.ieee.org/document/868501/}{\textit{IEEE
Trans.~Inf.~Theory}, \textbf{46}(6):2256--2261, 2000}], Mosonyi and Ogawa
[\href{https://doi.org/10.1007/s00220-017-2928-4/}{\textit{Commun.~Math.~Phys},
\textbf{355}(1):373--426, 2017}]. For the applications, we show that the strong
converse exponent in classical-quantum channel coding satisfies a minimax
identity. The established concavity is further employed to prove an entropic
duality between classical data compression with quantum side information and
classical-quantum channel coding, and a Fenchel duality in joint source-channel
coding with quantum side information in the forthcoming papers
Distributed Channel Synthesis
Two familiar notions of correlation are rediscovered as the extreme operating
points for distributed synthesis of a discrete memoryless channel, in which a
stochastic channel output is generated based on a compressed description of the
channel input. Wyner's common information is the minimum description rate
needed. However, when common randomness independent of the input is available,
the necessary description rate reduces to Shannon's mutual information. This
work characterizes the optimal trade-off between the amount of common
randomness used and the required rate of description. We also include a number
of related derivations, including the effect of limited local randomness, rate
requirements for secrecy, applications to game theory, and new insights into
common information duality.
Our proof makes use of a soft covering lemma, known in the literature for its
role in quantifying the resolvability of a channel. The direct proof
(achievability) constructs a feasible joint distribution over all parts of the
system using a soft covering, from which the behavior of the encoder and
decoder is inferred, with no explicit reference to joint typicality or binning.
Of auxiliary interest, this work also generalizes and strengthens this soft
covering tool.Comment: To appear in IEEE Trans. on Information Theory (submitted Aug., 2012,
accepted July, 2013), 26 pages, using IEEEtran.cl
Strong Converse and Second-Order Asymptotics of Channel Resolvability
We study the problem of channel resolvability for fixed i.i.d. input
distributions and discrete memoryless channels (DMCs), and derive the strong
converse theorem for any DMCs that are not necessarily full rank. We also
derive the optimal second-order rate under a condition. Furthermore, under the
condition that a DMC has the unique capacity achieving input distribution, we
derive the optimal second-order rate of channel resolvability for the worst
input distribution.Comment: 7 pages, a shorter version will appear in ISIT 2014, this version
includes the proofs of technical lemmas in appendice
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