TY - JOUR
T1 - Performance of quantized min-sum decoding algorithms for irregular LDPC codes
AU - Oh, Daesun
AU - Parhi, Keshab K.
PY - 2007
Y1 - 2007
N2 - In this paper, we analyze the performance of quantized min-sum decoding algorithms for irregular low-density parity-check (LDPC) codes. For regular LDPC codes, it is known that the normalized or offset min-sum decoding algorithm with quantization bits less than 6 bits achieves good performances over wide range of signal-to-noise ratios (SNR). However, finite precision effects in decoding irregular LDPC codes are different from that In decoding regular LDPC codes which is caused by the difference of convergence speeds between low degree nodes and high degree nodes. This paper proposes a novel method to improve the performance of the conventional normalized or offset min-sum decoding algorithm when it is approximated with finite precision for hardware Implementations. The proposed method applies down-scaling factors to Intrinsic Information which has effects on Increasing the reliability of extrinsic information at variable nodes and compensating the quantization errors caused by finite precision. Computer simulation results for Irregular LDPC codes show that our proposed normalized and offset min-sum decoding algorithms achieve much better performances at high SNR compared to the conventional normalized and offset min-sum algorithms under (6:2) quantization scheme.
AB - In this paper, we analyze the performance of quantized min-sum decoding algorithms for irregular low-density parity-check (LDPC) codes. For regular LDPC codes, it is known that the normalized or offset min-sum decoding algorithm with quantization bits less than 6 bits achieves good performances over wide range of signal-to-noise ratios (SNR). However, finite precision effects in decoding irregular LDPC codes are different from that In decoding regular LDPC codes which is caused by the difference of convergence speeds between low degree nodes and high degree nodes. This paper proposes a novel method to improve the performance of the conventional normalized or offset min-sum decoding algorithm when it is approximated with finite precision for hardware Implementations. The proposed method applies down-scaling factors to Intrinsic Information which has effects on Increasing the reliability of extrinsic information at variable nodes and compensating the quantization errors caused by finite precision. Computer simulation results for Irregular LDPC codes show that our proposed normalized and offset min-sum decoding algorithms achieve much better performances at high SNR compared to the conventional normalized and offset min-sum algorithms under (6:2) quantization scheme.
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U2 - 10.1109/iscas.2007.378624
DO - 10.1109/iscas.2007.378624
M3 - Conference article
AN - SCOPUS:34548822993
SN - 0271-4310
SP - 2758
EP - 2761
JO - Proceedings - IEEE International Symposium on Circuits and Systems
JF - Proceedings - IEEE International Symposium on Circuits and Systems
M1 - 4253249
T2 - 2007 IEEE International Symposium on Circuits and Systems, ISCAS 2007
Y2 - 27 May 2007 through 30 May 2007
ER -