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IEEE Region 10 Conference (TENCON)

Efficient check node update implementation for normalized min-sum algorithm

Lu Xin, Yongsheng Liang, Xu Jun

Published Oct 1, 2007• 2 citations
Publisher

Abstract

This paper has presented various min-sum related LDPC decoding algorithms and their typical hardware architectures of check node update in the scenario of parallel implementation. The m-to-2m decoder has been introduced to generate more efficient new hardware implementations of check node update, which can obviously reduce the number of multiplication operations for normalized min-sum algorithm for high rate LDPC codes. Simulations have claimed the performance of normalized min-sum is nearly the same as that of Log-BP, namely the optimal algorithm. In general, this paper has proved that normalized min-sum is good choices as LDPC decoding methods.

DOI
10.1109/tencon.2007.4429021

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This paper has presented various min-sum related LDPC decoding algorithms and their typical hardware architectures of check node update in the scenario of parallel implementation. The m-to-2m decoder has been introduced to generate more efficient new hardware implementations of check node update, which can obviously reduce the number of multiplication operations for normalized min-sum algorithm for high rate LDPC codes. Simulations have claimed the performance of normalized min-sum is nearly the same as that of Log-BP, namely the optimal algorithm. In general, this paper has proved that normalized min-sum is good choices as LDPC decoding methods.
This paper has presented various min-sum related LDPC decoding algorithms and their typical hardware architectures of check node update in the scenario of parallel implementation. For one check node update of normalized min-sum algorithm, if the current row weight is dc, dcmultiplications are needed. If dcis large, dcmultiplications are needed, which leads to high complexity. In this article, one innovative method for check node update has been found, which can obviously reduce the number of multiplication operations for the normalized min-sum algorithm and the number of comparison/selection operations for the row weight matched min-sum algorithm of high rate LDPC codes. Simulations have claimed the performance of normalized min-sum and row weight matched min-sum is nearly the same as that of Log-BP, namely the optimal algorithm, which has shown that normalized min-sum and row weight matched min-sum are good choices for LDPC.
This paper has presented various Min-Sum related LDPC decoding algorithms and their typical hardware architectures of check node update in the scenario of parallel implementation. For one check node update of Normalized Min-Sum algorithm, if the current row weight is dc, dc multiplications are needed. If dc is large, dc multiplications are needed, which leads to high complexity. In this article, one innovative method for check node update has been found, which can obviously reduce the number of comparison/selection operations for the row weight matched Min-Sum algorithm of high rate LDPC codes. Simulations have claimed the performance of row weight matched Min-Sum is nearly the same as that of Log-BP, namely the optimal algorithm, which has shown that row weight matched Min-Sum are good choices for LDPC decoding.
This paper presents various Min-Sum-related LDPC decoding algorithms and their typical hardware architectures of check node update in the scenario of parallel implementation.For one check node update of Normalized Min-Sum algorithm(Nor-MS),if the current row weight is d c,d cmultiplications are needed.If d cis large,this would lead to high complexity.An innovative method for check node update is proposed,which could obviously reduce the number of multiplication operations for the Nor-MS algorithm and the number of comparison/selection operations for the Rwm-MS algorithm of high rate LDPC codes.Simulations indicate that the performance of Nor-MS and Rwm-MS is almost the same as that of Log-BP,namely the optimal algorithm,and that Nor-MS and Rwm-MS are good choices for LDPC decoding.
This paper has presented various Min-Sum related LDPC decoding algorithms and their typical hardware architectures of check node update in the scenario of parallel implementation. For one check node update of Normalized Min-Sum algorithm, if the current row weight is dc, dc multiplications are needed. If dc is large, dc multiplications are needed, which leads to high complexity. In this article, one innovative method for check node update has been found, which can obviously reduce the number of comparison/selection operations for the row weight matched Min-Sum algorithm of high rate LDPC codes. Simulations have claimed the performance of row weight matched Min-Sum is nearly the same as that of Log-BP, namely the optimal algorithm, which has shown that row weight matched Min-Sum are good choices for LDPC decoding.
In this paper, the partial-min algorithms are proposed for updating the check node messages in decoding low-density parity-check (LDPC) codes. The proposed methods can achieve a good performance-versus-complexity tradeoff by reducing both the check node processor complexity and the decoder memory requirements. Float-point and fixed-point simulation results show that a near optimum performance can be maintained. The architecture of a check node processor implementing the partial-min algorithms is also presented. The hardware expense especially the memory requirement is analyzed and compared with normal decoding architectures
To improve the decoding performance of Low Density Parity Check(LDPC) code,an efficient decoding algorithm based on min-sum algorithm is proposed. The proposed algorithm analyzes the check node update equation and at the view of probability to form an approximation min-sum equation. It adopts a dynamic normalization factor to improve the decoding performance. Detailed simulation results and comparisons with Belief Propagation(BP) algorithm show that,with little decoding performance loss,the proposed algorithm not only decreases computational load,but also improves decoding efficiency.