IEEE International Symposium on Information Theory (ISIT)

Threshold-based design of quantized decoder for LDPC codes

Yucheng He, Hong-Pei Li, Shaohui Sun, Lei Li

Published Jan 1, 2003• 3 citations

Abstract

The effects of quantization on the sum-product (SP) algorithm for iteratively decoding low-density parity-check (LDPC) codes are investigated. Two threshold-based designs of the quantized sum-product (Q-SP) algorithm are presented together with the results of the 4-bit decoding for several known regular codes over the additive white Gaussian noise channel. Nonuniform quantization can significantly improve the Q-SP decoding performance without increase in implementation complexity.

DOI: 10.1109/isit.2003.1228163

Related papers

On implementation of min-sum algorithm for decoding low-density parity-check (LDPC) codes

Farhad Zarkeshvari, Amir H. Banihashemi

This paper is concerned with the implementation issues of the so-called min-sum algorithm (also referred to as max-sum or max-product) for the decoding of low-density parity-check (LDPC) codes. The effects of clipping threshold and the number of quantization bits on the performance of the min-sum algorithm at short and intermediate block lengths are studied. It is shown that min-sum is robust against quantization effects, and in many cases, only four quantization bits suffices to obtain close to ideal performance. We also propose modifications to the min-sum algorithm that improve the performance by a few tenths of a dB with just a small increase in decoding complexity.

GlobecomMar 22, 200449 citations

Performance of Quantized Min-Sum Decoding Algorithms for Irregular LDPC Codes

Daesun Oh, Keshab K. Parhi

This paper analyzes 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 the proposed normalized and offset min-sum decoding algorithms achieve better performances at high SNR compared to the conventional normalized and offset min-sum algorithms under (6:2) quantization scheme.

IEEE International Symposium on Circuits and Systems proceedingsMay 1, 200715 citations

Algorithm Design of Quantized Min-Sum Decoder for Low-density Parity-Check (LDPC) Codes Based on Density Evolution

Xinmei Wang

Design of quantized Min-Sum decoder for low-density parity-check (LDPC) codes is very important for the application of LDPC codes.Based on the theory of density evolution,the paper proposes an algorithm to optimize the uniform or ununiform quantized Min-Sum decoder for regular or irregular LDPC codes.Compared with the continuous Sum-Product decoder,the proposed quantized decoder can reduce computational cost with little performance degradation, involving only integer addition calculations.

Jan 1, 20050 citations

Gaussian approximation for sum-product decoding of low-density parity-check codes

Sae-Young Chung, Rudiger L. Urbanke, Tom Richardson

We use a Gaussian approximation (GA) for analyzing the sum-product algorithm for low-density parity-check (LDPC) codes and memoryless binary-input continuous-output additive white Gaussian noise (AWGN) channels. This simplification allows us to calculate the threshold quickly and to understand the behavior of the decoder better. We have also designed high rate LDPC codes using the Gaussian approximation that have thresholds less than 0.05 dB from the Shannon limit.

IEEE International Symposium on Information Theory (ISIT)Nov 7, 200220 citations

Improvement of the forced-convergence decoding for LDPC codes

Rumi Aoyama, Yuichi Kaji

An improvement of the forced-convergence decoding algorithm is discussed. Iterative decoding schemes such as the sum-product (SP) decoding bring out excellent performance of low-density parity check (LDPC) codes. The forced-convergence (FC) decoding is also an iterative decoding algorithm obtained by modifying the SP decoding. The FC decoding succeeds to reduce the decoding complexity of the SP decoding, but the error correcting performance is severely degraded by the modification. The performance degradation of the FC decoding is expected to be mitigated if the convergence criterion used in the algorithm is appropriately modified. This paper discusses several types of modifications of the convergence criteria. Computer simulation shows that the proposed modifications improve the performance of the FC decoding algorithm.

International Symposium on Information Theory and its ApplicationsDec 1, 20088 citations

Some Quantization Issues for Decoding of Low-Density Parity Check Codes with BP Algorithm

Sun Shao

The quantization scheme for the iterative belief propagation (BP) algorithm of low density parity check (LDPC) codes is studied.The effects of the bound,resolution,and other parameters for uniform and nonuniform quantization schemes are discussed.Good performance of decoding under properly designed quantization can be obtained as shown by simulations.Furthermore,we found that the quantization of high order for the mid variables of BP decoding algorithm can improve the performance and reduce the error floor of LDPC codes at regions of high signal to noise ratio.

Dianzi xuebaoJan 1, 20030 citations