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Jun Muramatsu

Publications and source records attributed to Jun Muramatsu.

At least 19 recordsLinked to original sources
arXiv2024

Distributed Source Coding, Multiple Description Coding, and Source Coding with Side Information at Decoders Using Constrained-Random Number Generators

This paper investigates a unification of distributed source coding, multiple description coding, and source coding with side information at decoders. The equivalence between the multiple-decoder extension of distributed source coding with decoder side information and the multiple-source extension of multiple description coding with decoder side information is clarified. Their multi-letter rate-distortion region for arbitrary general correlated sources is characterized in terms of entropy functions. We construct a code based on constrained-random number generators and show its achievability.

cs.IT↗
arXiv2024

Distributed Source Coding Using Constrained-Random-Number Generators

This paper investigates the general distributed lossless/lossy source coding formulated by Jana and Blahut. Their multi-letter rate-distortion region, an alternative to the region derived by Yang and Qin, is characterized by entropy functions for arbitrary general correlated sources. Achievability is shown by constructing a code based on constrained-random number generators.

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arXiv2023

Channel Codes for Relayless Networks with General Message Access Structure

Channel codes for relayless networks with the general message access structure is introduced. It is shown that the multi-letter characterized capacity region of this network is achievable with this code. The capacity region is characterized in terms of entropy functions and provides an alternative to the regions introduced by [Somekh-Baruch and Verdú, ISIT2006][Muramatsu and Miyake, ISITA2018].

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arXiv2022

A Probabilistic Shaping Approach for Optical Region-of-Interest Signaling

We propose a probabilistic shaping approach for region-of-interest signaling, where a low-rate signal controls the desired probabilistic ranges of a high-rate data stream using a flexible distribution controller. In addition, we introduce run-length-aware values for frozen bit indices in systematic polar code to minimize the run-length without using run-length-limited code. Our compact system can support soft-decision forward-error-correction decoding with excellent spectral efficiency compared with related work based on hybrid modulation schemes.

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arXiv2022

On the Achievability of Interference Channel Coding

This paper investigates the achievability of the interference channel coding. It is clarified that the rate-splitting technique is unnecessary to achieve Han-Kobayashi and Jian-Xin-Garg inner regions. Codes are constructed by using sparse matrices (with logarithmic column degree) and the constrained-random-number generators. By extending the problem, we can establish a possible extension of known inner regions.

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arXiv2022

Binary Polar Codes Based on Bit Error Probability

This paper introduces techniques to construct binary polar source/channel codes based on the bit error probability of successive-cancellation decoding. The polarization lemma is reconstructed based on the bit error probability and then techniques to compute the bit error probability are introduced. These techniques can be applied to the construction of polar codes and the computation of lower and upper bounds of the block decoding error probability.

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arXiv2019

Proceedings of the 11th Asia-Europe Workshop on Concepts in Information Theory

This year, 2019 we celebrate 30 years of our friendship between Asian and European scientists at the AEW11 in Rotterdam, the Netherlands. Many of the 1989 participants are also present at the 2019 event. This year we have many participants from different parts of Asia and Europe. It shows the importance of this event. It is a good tradition to pay a tribute to a special lecturer in our community. This year we selected Hiroyoshi Morita, who is a well known information theorist with many original contributions.

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arXiv2019

Successive-Cancellation Decoding of Linear Source Code

This paper investigates the error probability of several decoding methods for a source code with decoder side information, where the decoding methods are: 1) symbol-wise maximum a posteriori decoding, 2) successive-cancellation decoding, and 3) stochastic successive-cancellation decoding. The proof of the effectiveness of a decoding method is reduced to that for an arbitrary decoding method, where `effective' means that the error probability goes to zero as $n$ goes to infinity. Furthermore, we revisit the polar source code showing that stochastic successive-cancellation decoding, as well as successive-cancellation decoding, is effective for this code.

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arXiv2018

Multi-Terminal Codes Using Constrained-Random-Number Generators

A general multi-terminal source code and a general multi-terminal channel code are presented. Constrained-random-number generators with sparse matrices, which are building blocks for the code construction, are used in the construction of both encoders and decoders. Achievable regions for source coding and channel coding are derived in terms of entropy functions, where the capacity region for channel coding provides an alternative to the region of [Somekh-Baruch and Verdú, ISIT2006].

cs.IT↗
arXiv2017

Proceedings of Workshop AEW10: Concepts in Information Theory and Communications

The 10th Asia-Europe workshop in "Concepts in Information Theory and Communications" AEW10 was held in Boppard, Germany on June 21-23, 2017. It is based on a longstanding cooperation between Asian and European scientists. The first workshop was held in Eindhoven, the Netherlands in 1989. The idea of the workshop is threefold: 1) to improve the communication between the scientist in the different parts of the world; 2) to exchange knowledge and ideas; and 3) to pay a tribute to a well respected and special scientist.

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arXiv2017

On the error probability of stochastic decision and stochastic decoding

This paper investigates the error probability of a stochastic decision and the way in which it differs from the error probability of an optimal decision, i.e., the maximum a posteriori decision. This paper calls attention to the fact that the error probability of a stochastic decision with the a posteriori distribution is at most twice the error probability of the maximum a posteriori decision. It is shown that, by generating an independent identically distributed random sequence subject to the a posteriori distribution and making a decision that maximizes the a posteriori probability over the sequence, the error probability approaches exponentially the error probability of the maximum a posteriori decision as the sequence length increases. Using these ideas as a basis, we can construct stochastic decoders for source/channel codes.

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arXiv2017

Construction of a Channel Code from an Arbitrary Source Code with Decoder Side Information

The construction of a channel code by using a source code with decoder side information is introduced. For the construction, any pair of encoder and decoder is available for a source code with decoder side information. A constrained-random-number generator, which generates random numbers satisfying a condition specified by a function and its value, is used to construct a stochastic channel encoder. The result suggests that we can divide the channel coding problem into the problems of channel encoding and source decoding with side information.

cs.IT↗
arXiv2013

Channel Coding and Lossy Source Coding Using a Constrained Random Number Generator

Stochastic encoders for channel coding and lossy source coding are introduced with a rate close to the fundamental limits, where the only restriction is that the channel input alphabet and the reproduction alphabet of the lossy source code are finite. Random numbers, which satisfy a condition specified by a function and its value, are used to construct stochastic encoders. The proof of the theorems is based on the hash property of an ensemble of functions, where the results are extended to general channels/sources and alternative formulas are introduced for channel capacity and the rate-distortion region. Since an ensemble of sparse matrices has a hash property, we can construct a code by using sparse matrices, where the sum-product algorithm can be used for encoding and decoding by assuming that channels/sources are memoryless.

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arXiv2013

Construction of Slepian-Wolf Source Code and Broadcast Channel Code Based on Hash Property

The aim of this paper is to prove theorems for the Slepian-Wolf source coding and the broadcast channel coding (independent messages and no common message) based on the the notion of a stronger version of the hash property for an ensemble of functions. Since an ensemble of sparse matrices has a strong hash property, codes using sparse matrices can realize the achievable rate region. Furthermore, extensions to the multiple source coding and multiple output broadcast channel coding are investigated.

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arXiv2012

Construction of Multiple Access Channel Codes Based on Hash Property

The aim of this paper is to introduce the construction of codes for a general discrete stationary memoryless multiple access channel based on the the notion of the hash property. Since an ensemble of sparse matrices has a hash property, we can use sparse matrices for code construction. Our approach has a potential advantage compared to the conventional random coding because it is expected that we can use some approximation algorithms by using the sparse structure of codes.

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arXiv2010

Construction of Codes for Wiretap Channel and Secret Key Agreement from Correlated Source Outputs by Using Sparse Matrices

The aim of this paper is to prove coding theorems for the wiretap channel coding problem and secret key agreement problem based on the the notion of a hash property for an ensemble of functions. These theorems imply that codes using sparse matrices can achieve the optimal rate. Furthermore, fixed-rate universal coding theorems for a wiretap channel and a secret key agreement are also proved.

cs.IT↗
arXiv2009

Hash Property and Coding Theorems for Sparse Matrices and Maximum-Likelihood Coding

The aim of this paper is to prove the achievability of several coding problems by using sparse matrices (the maximum column weight grows logarithmically in the block length) and maximal-likelihood (ML) coding. These problems are the Slepian-Wolf problem, the Gel'fand-Pinsker problem, the Wyner-Ziv problem, and the One-helps-one problem (source coding with partial side information at the decoder). To this end, the notion of a hash property for an ensemble of functions is introduced and it is proved that an ensemble of $q$-ary sparse matrices satisfies the hash property. Based on this property, it is proved that the rate of codes using sparse matrices and maximal-likelihood (ML) coding can achieve the optimal rate.

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