Optimization of scaling soft information in iterative decoding via density evolution methods

Jun Heo; Keith M. Chugg

doi:10.1109/TCOMM.2005.849782

Optimization of scaling soft information in iterative decoding via density evolution methods

Jun Heo, Keith M. Chugg

Research output: Contribution to journal › Article › peer-review

29 Citations (Scopus)

Abstract

Density evolution has recently been used to analyze iterative decoding and explain many characteristics of iterative decoding including convergence of performance and preferred structures for the constituent codes. The scaling of extrinsic information (messages) has been heuristically used to enhance the performance in the iterative decoding literature, particularly based on the min-sum message passing algorithm. In this paper, it is demonstrated that density evolution can be used to obtain the optimal scaling factor and also estimate the maximum achievable scaling gain. For low density parity check (LDPC) codes and serially concatenated convolutional codes (SCCC) with two-state constituent codes, the analytic density evolution technique is used, while the signal-to-noise ratio (SNR) evolution technique and the EXIT chart technique is used for SCCC with more than 2 state constituent codes. Simulation results show that the scaling gain predicted by density evolution or SNR evolution matches well with the scaling gain observed by simulation.

Original language	English
Pages (from-to)	957-961
Number of pages	5
Journal	IEEE Transactions on Communications
Volume	53
Issue number	6
DOIs	https://doi.org/10.1109/TCOMM.2005.849782
Publication status	Published - 2005 Jun
Externally published	Yes

Bibliographical note

Funding Information:
Paper approved by H. El-Gamal, the Editor for Space-Time Coding and Spread Spectrum of the IEEE Communications Society. Manuscript received October 1, 2002; revised March 1, 2004. This work was supported in part by Grant R08-2003-000-10165-0 from the Basic Research Program of the Korea Science and Engineering Foundation and by the National Science Foundation under CCR-0082987.

Keywords

Density evolution
Iterative decoding
Low-density parity-check (LDPC) codes
Serially concatenated convolutional codes (SCCC)

ASJC Scopus subject areas

Electrical and Electronic Engineering

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10.1109/TCOMM.2005.849782

Cite this

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title = "Optimization of scaling soft information in iterative decoding via density evolution methods",

abstract = "Density evolution has recently been used to analyze iterative decoding and explain many characteristics of iterative decoding including convergence of performance and preferred structures for the constituent codes. The scaling of extrinsic information (messages) has been heuristically used to enhance the performance in the iterative decoding literature, particularly based on the min-sum message passing algorithm. In this paper, it is demonstrated that density evolution can be used to obtain the optimal scaling factor and also estimate the maximum achievable scaling gain. For low density parity check (LDPC) codes and serially concatenated convolutional codes (SCCC) with two-state constituent codes, the analytic density evolution technique is used, while the signal-to-noise ratio (SNR) evolution technique and the EXIT chart technique is used for SCCC with more than 2 state constituent codes. Simulation results show that the scaling gain predicted by density evolution or SNR evolution matches well with the scaling gain observed by simulation.",

keywords = "Density evolution, Iterative decoding, Low-density parity-check (LDPC) codes, Serially concatenated convolutional codes (SCCC)",

author = "Jun Heo and Chugg, {Keith M.}",

note = "Funding Information: Paper approved by H. El-Gamal, the Editor for Space-Time Coding and Spread Spectrum of the IEEE Communications Society. Manuscript received October 1, 2002; revised March 1, 2004. This work was supported in part by Grant R08-2003-000-10165-0 from the Basic Research Program of the Korea Science and Engineering Foundation and by the National Science Foundation under CCR-0082987.",

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doi = "10.1109/TCOMM.2005.849782",

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N2 - Density evolution has recently been used to analyze iterative decoding and explain many characteristics of iterative decoding including convergence of performance and preferred structures for the constituent codes. The scaling of extrinsic information (messages) has been heuristically used to enhance the performance in the iterative decoding literature, particularly based on the min-sum message passing algorithm. In this paper, it is demonstrated that density evolution can be used to obtain the optimal scaling factor and also estimate the maximum achievable scaling gain. For low density parity check (LDPC) codes and serially concatenated convolutional codes (SCCC) with two-state constituent codes, the analytic density evolution technique is used, while the signal-to-noise ratio (SNR) evolution technique and the EXIT chart technique is used for SCCC with more than 2 state constituent codes. Simulation results show that the scaling gain predicted by density evolution or SNR evolution matches well with the scaling gain observed by simulation.

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Optimization of scaling soft information in iterative decoding via density evolution methods

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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