1,083 research outputs found
Alphabet Sizes of Auxiliary Variables in Canonical Inner Bounds
Alphabet size of auxiliary random variables in our canonical description is
derived. Our analysis improves upon estimates known in special cases, and
generalizes to an arbitrary multiterminal setup. The salient steps include
decomposition of constituent rate polytopes into orthants, translation of a
hyperplane till it becomes tangent to the achievable region at an extreme
point, and derivation of minimum auxiliary alphabet sizes based on
Caratheodory's theorem.Comment: 20 pages, no figures, explanation of a part of impending IEEE IT
submissio
A Generalized Typicality for Abstract Alphabets
A new notion of typicality for arbitrary probability measures on standard
Borel spaces is proposed, which encompasses the classical notions of weak and
strong typicality as special cases. Useful lemmas about strong typical sets,
including conditional typicality lemma, joint typicality lemma, and packing and
covering lemmas, which are fundamental tools for deriving many inner bounds of
various multi-terminal coding problems, are obtained in terms of the proposed
notion. This enables us to directly generalize lots of results on finite
alphabet problems to general problems involving abstract alphabets, without any
complicated additional arguments. For instance, quantization procedure is no
longer necessary to achieve such generalizations. Another fundamental lemma,
Markov lemma, is also obtained but its scope of application is quite limited
compared to others. Yet, an alternative theory of typical sets for Gaussian
measures, free from this limitation, is also developed. Some remarks on a
possibility to generalize the proposed notion for sources with memory are also
given.Comment: 44 pages; submitted to IEEE Transactions on Information Theor
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
Classical capacity of bosonic broadcast communication and a new minimum output entropy conjecture
Previous work on the classical information capacities of bosonic channels has
established the capacity of the single-user pure-loss channel, bounded the
capacity of the single-user thermal-noise channel, and bounded the capacity
region of the multiple-access channel. The latter is a multi-user scenario in
which several transmitters seek to simultaneously and independently communicate
to a single receiver. We study the capacity region of the bosonic broadcast
channel, in which a single transmitter seeks to simultaneously and
independently communicate to two different receivers. It is known that the
tightest available lower bound on the capacity of the single-user thermal-noise
channel is that channel's capacity if, as conjectured, the minimum von Neumann
entropy at the output of a bosonic channel with additive thermal noise occurs
for coherent-state inputs. Evidence in support of this minimum output entropy
conjecture has been accumulated, but a rigorous proof has not been obtained. In
this paper, we propose a new minimum output entropy conjecture that, if proved
to be correct, will establish that the capacity region of the bosonic broadcast
channel equals the inner bound achieved using a coherent-state encoding and
optimum detection. We provide some evidence that supports this new conjecture,
but again a full proof is not available.Comment: 13 pages, 7 figure
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