Performance Analysis of Raptor Codes under Maximum Likelihood Decoding

Peng Wang; Guoqiang Mao; Zihuai Lin; Ming Ding; Weifa Liang; Xiaohu Ge; Zhiyun Lin

doi:10.1109/TCOMM.2016.2522403

Performance Analysis of Raptor Codes under Maximum Likelihood Decoding

Peng Wang
, Guoqiang Mao
, Zihuai Lin
, Ming Ding
, Weifa Liang
, Xiaohu Ge^*
, Zhiyun Lin

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

16 Citations (Scopus)

Abstract

In this paper, we analyze the maximum likelihood decoding performance of Raptor codes with a systematic low-density generator-matrix code as the pre-code. By investigating the rank of the product of two random coefficient matrices, we derive upper and lower bounds on the decoding failure probability. The accuracy of our analysis is validated through simulations. Results of extensive Monte Carlo simulations demonstrate that for Raptor codes with different degree distributions and pre-codes, the bounds obtained in this paper are of high accuracy. The derived bounds can be used to design near-optimum Raptor codes with short and moderate lengths.

Original language	English
Article number	7393818
Pages (from-to)	906-917
Journal	IEEE Transactions on Communications
Volume	64
Issue number	3
DOIs	https://doi.org/10.1109/TCOMM.2016.2522403
Publication status	Published - 1 Mar 2016
Externally published	Yes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Research Keywords

asymptotic analysis
decoding failure probability
maximum likelihood (ML) decoding
Raptor codes

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title = "Performance Analysis of Raptor Codes under Maximum Likelihood Decoding",

abstract = "In this paper, we analyze the maximum likelihood decoding performance of Raptor codes with a systematic low-density generator-matrix code as the pre-code. By investigating the rank of the product of two random coefficient matrices, we derive upper and lower bounds on the decoding failure probability. The accuracy of our analysis is validated through simulations. Results of extensive Monte Carlo simulations demonstrate that for Raptor codes with different degree distributions and pre-codes, the bounds obtained in this paper are of high accuracy. The derived bounds can be used to design near-optimum Raptor codes with short and moderate lengths.",

keywords = "asymptotic analysis, decoding failure probability, maximum likelihood (ML) decoding, Raptor codes",

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AU - Wang, Peng

AU - Mao, Guoqiang

AU - Lin, Zihuai

AU - Ding, Ming

AU - Liang, Weifa

AU - Ge, Xiaohu

AU - Lin, Zhiyun

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

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Y1 - 2016/3/1

N2 - In this paper, we analyze the maximum likelihood decoding performance of Raptor codes with a systematic low-density generator-matrix code as the pre-code. By investigating the rank of the product of two random coefficient matrices, we derive upper and lower bounds on the decoding failure probability. The accuracy of our analysis is validated through simulations. Results of extensive Monte Carlo simulations demonstrate that for Raptor codes with different degree distributions and pre-codes, the bounds obtained in this paper are of high accuracy. The derived bounds can be used to design near-optimum Raptor codes with short and moderate lengths.

AB - In this paper, we analyze the maximum likelihood decoding performance of Raptor codes with a systematic low-density generator-matrix code as the pre-code. By investigating the rank of the product of two random coefficient matrices, we derive upper and lower bounds on the decoding failure probability. The accuracy of our analysis is validated through simulations. Results of extensive Monte Carlo simulations demonstrate that for Raptor codes with different degree distributions and pre-codes, the bounds obtained in this paper are of high accuracy. The derived bounds can be used to design near-optimum Raptor codes with short and moderate lengths.

KW - asymptotic analysis

KW - decoding failure probability

KW - maximum likelihood (ML) decoding

KW - Raptor codes

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U2 - 10.1109/TCOMM.2016.2522403

DO - 10.1109/TCOMM.2016.2522403

M3 - RGC 21 - Publication in refereed journal

SN - 0090-6778

VL - 64

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EP - 917

JO - IEEE Transactions on Communications

JF - IEEE Transactions on Communications

IS - 3

M1 - 7393818

ER -

Performance Analysis of Raptor Codes under Maximum Likelihood Decoding

Abstract

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Research Keywords

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