| Project/Area Number |
15560338
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Communication/Network engineering
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| Research Institution | Waseda University |
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Principal Investigator |
MATSUSHIMA Toshiyasu Waseda University, School of Science and Engineering, Professor, 理工学術院, 教授 (30219430)
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| Co-Investigator(Kenkyū-buntansha) |
HIRASAWA Shigeichi Waseda University, School of Science and Engineering, Professor, 理工学術院, 教授 (30147946)
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| Project Period (FY) |
2003 – 2005
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| Project Status |
Completed (Fiscal Year 2005)
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| Budget Amount *help |
¥3,700,000 (Direct Cost: ¥3,700,000)
Fiscal Year 2005: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2004: ¥1,300,000 (Direct Cost: ¥1,300,000)
Fiscal Year 2003: ¥1,600,000 (Direct Cost: ¥1,600,000)
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| Keywords | Information Theory / Error Correcting Code / Decoding Algorithms / Calculation of Posterior Probability / Graphical Model / Knowledge Information Processing / Uncertain Reasoning / 復号アルゴリズム |
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| Research Abstract |
For its good error correcting ability, Turbo code and Turbo decoding algorithm, which was proposed in the research area of the error correcting codes, have gotten a lot of attention recently as important technology of 21st century's reliable communication and accumulation technique. It have been known that the iterative decoding algorithms which includes Turbo decoding algorithm can be regarded as applications of Belief Propagation algorithm, which was proposed in the research area of the knowledge information processing as uncertain reasoning algorithm. It can be interpreted that the turbo decoding algorithm calculates the approximation of posterior probability effectively.
It was obtained that the Extended Junction Graph (EJG) and the parallel iterative algorithm for calculating posterior probability, which are results achieved in "The Analysis and Design of the Reliable Iterative Decoding Algorithm based on Uncertain Reasoning" which is supported by Grant-in-Aid for Scientific Research in 2000-2002, can deal with general probabilistic models compared to existing belief propagation algorithm and retain the higher performance even when the graph has loops (almost of the practical codes have loops).
In this research, we have done following,
(1)We applied the EJG and the parallel iterative algorithm to the LDPC codes which have some small loops, we sought further efficiency and analyzed the performance.
(2)We applied the algorithm to the channel models which have feedback channel such as ARQ Scheme and analyzed the performance.
(3)Moreover, we applied the algorithm to the decryption of stream cipher and analyzed the performance.
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