Self-compensation technique for simplified belief-propagation algorithm

Yen Chin Liao*, Chien Ching Lin, Hsie-Chia Chang, Chih-Wei Liu

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    8 Scopus citations


    The min-sum algorithm is the most common method to simplify the belief-propagation algorithm for decoding low-density parity-check (LDPC) codes. However, there exists a performance gap between the min-sum and belief-propagation algorithms due to nonlinear approximation. In this paper, a self-compensation technique using dynamic normalization is thus proposed to improve the approximation accuracy. The proposed scheme scales the min-sum algorithm by a dynamic factor that can be derived theoretically from order statistics. Moreover, applying the proposed technique to several LDPC codes for DVB-S2 system, the average signal-to-noise ratio degradation, which results from approximation inaccuracy and quantization error, is reduced to 0.2 dB. Not only does it enhance the error-correcting capability of the min-sum algorithm, but the proposed self-compensation technique also preserves a modest hardware cost. After realized with 0.13-μm standard cell library, the dynamic normalization requires about 100 additional gates for each check node unit in the min-sum algorithm.

    Original languageEnglish
    Pages (from-to)3061-3072
    Number of pages12
    JournalIEEE Transactions on Signal Processing
    Issue number6 II
    StatePublished - 1 Jun 2007


    • Belief-propagation
    • Dynamic normalization
    • Iterative decoding
    • Low-density parity-check (LDPC) codes
    • Min-sum algorithm
    • Self compensation


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