Dependable Static PPM Code Using LDPC Code

In recent years, with the increasing amount of data processed by computers, data compression technology is used to reduce data traffic and storage requirements. However, compressed data is vulnerable to errors. To address this issue, various data compression methods with error correction functions have been proposed. Among these error-correcting codes, LDPC code is notable for achieving a rate very close to the Shannon limit, the theoretical upper bound of information transmission. In this research, we propose a method to improve error correction capability when encoding data compressed by Static PPM, an extension of PPM compression, allowing partial decompression using LDPC codes. When large errors occur, Error correcting codes fail to correct them. However, this method can present multiple candidates, including large errors, during decoding. This allows the correct error to be selected from the candidates.

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Publication Details

Published
2025-03-29
DOI
https://doi.org/10.1109/icct-pacific63901.2025.11012791
Primary Topic
Modular Robots and Swarm Intelligence
Type
article
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article

Dependable Static PPM Code Using LDPC Code

Masato Kitakami, Masahiko Suzuki
Modular Robots and Swarm Intelligence
article

Dependable Static PPM Code Using LDPC Code

Masato Kitakami, Masahiko Suzuki
article en

Abstract

In recent years, with the increasing amount of data processed by computers, data compression technology is used to reduce data traffic and storage requirements. However, compressed data is vulnerable to errors. To address this issue, various data compression methods with error correction functions have been proposed. Among these error-correcting codes, LDPC code is notable for achieving a rate very close to the Shannon limit, the theoretical upper bound of information transmission. In this research, we propose a method to improve error correction capability when encoding data compressed by Static PPM, an extension of PPM compression, allowing partial decompression using LDPC codes. When large errors occur, Error correcting codes fail to correct them. However, this method can present multiple candidates, including large errors, during decoding. This allows the correct error to be selected from the candidates.

Chiba University (JP)
Openalex Percentile: Top 88%
Modular Robots and Swarm Intelligence
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