Self-compensation technique for simplified belief-propagation algorithm

Abstract

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-mu m standard cell library, the dynamic normalization requires about 100 additional gates for each check,node unit in the min-sum algorithm.