The search functionality is under construction.

The search functionality is under construction.

In this paper, we present a new soft-decision iterative decoding algorithm using an efficient minimum distance search (MDS) algorithm. The proposed MDS algorithm is a *top-down and recursive* MDS algorithm, which finds a most likely codeword among the codewords at the minimum distance of the code from a given codeword. A search is made in each divided section by a "call by need" from the upper section. As a consequence, the search space and computational complexity are reduced significantly. The simulation results show that the proposed decoding algorithm achieves near error performance to the maximum likelihood decoding for any RM code of length 128 and suboptimal for the (256, 37), (256, 93) and (256, 163) RM codes.

- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E85-A No.10 pp.2220-2228

- Publication Date
- 2002/10/01

- Publicized

- Online ISSN

- DOI

- Type of Manuscript
- Special Section PAPER (Special Section on Information Theory and Its Applications)

- Category
- Coding Theory

The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.

Copy

Jun ASATANI, Kenichi TOMITA, Takuya KOUMOTO, Toyoo TAKATA, Tadao KASAMI, "A Soft-Decision Iterative Decoding Algorithm Using a Top-Down and Recursive Minimum Distance Search" in IEICE TRANSACTIONS on Fundamentals,
vol. E85-A, no. 10, pp. 2220-2228, October 2002, doi: .

Abstract: In this paper, we present a new soft-decision iterative decoding algorithm using an efficient minimum distance search (MDS) algorithm. The proposed MDS algorithm is a *top-down and recursive* MDS algorithm, which finds a most likely codeword among the codewords at the minimum distance of the code from a given codeword. A search is made in each divided section by a "call by need" from the upper section. As a consequence, the search space and computational complexity are reduced significantly. The simulation results show that the proposed decoding algorithm achieves near error performance to the maximum likelihood decoding for any RM code of length 128 and suboptimal for the (256, 37), (256, 93) and (256, 163) RM codes.

URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e85-a_10_2220/_p

Copy

@ARTICLE{e85-a_10_2220,

author={Jun ASATANI, Kenichi TOMITA, Takuya KOUMOTO, Toyoo TAKATA, Tadao KASAMI, },

journal={IEICE TRANSACTIONS on Fundamentals},

title={A Soft-Decision Iterative Decoding Algorithm Using a Top-Down and Recursive Minimum Distance Search},

year={2002},

volume={E85-A},

number={10},

pages={2220-2228},

abstract={In this paper, we present a new soft-decision iterative decoding algorithm using an efficient minimum distance search (MDS) algorithm. The proposed MDS algorithm is a *top-down and recursive* MDS algorithm, which finds a most likely codeword among the codewords at the minimum distance of the code from a given codeword. A search is made in each divided section by a "call by need" from the upper section. As a consequence, the search space and computational complexity are reduced significantly. The simulation results show that the proposed decoding algorithm achieves near error performance to the maximum likelihood decoding for any RM code of length 128 and suboptimal for the (256, 37), (256, 93) and (256, 163) RM codes.},

keywords={},

doi={},

ISSN={},

month={October},}

Copy

TY - JOUR

TI - A Soft-Decision Iterative Decoding Algorithm Using a Top-Down and Recursive Minimum Distance Search

T2 - IEICE TRANSACTIONS on Fundamentals

SP - 2220

EP - 2228

AU - Jun ASATANI

AU - Kenichi TOMITA

AU - Takuya KOUMOTO

AU - Toyoo TAKATA

AU - Tadao KASAMI

PY - 2002

DO -

JO - IEICE TRANSACTIONS on Fundamentals

SN -

VL - E85-A

IS - 10

JA - IEICE TRANSACTIONS on Fundamentals

Y1 - October 2002

AB - In this paper, we present a new soft-decision iterative decoding algorithm using an efficient minimum distance search (MDS) algorithm. The proposed MDS algorithm is a *top-down and recursive* MDS algorithm, which finds a most likely codeword among the codewords at the minimum distance of the code from a given codeword. A search is made in each divided section by a "call by need" from the upper section. As a consequence, the search space and computational complexity are reduced significantly. The simulation results show that the proposed decoding algorithm achieves near error performance to the maximum likelihood decoding for any RM code of length 128 and suboptimal for the (256, 37), (256, 93) and (256, 163) RM codes.

ER -