CAJ | 학술논문

介绍了低密度奇偶校验码（LDPC）的几种常用译码算法及其优缺点,特别用密度进化理论分析了归一化置信传播（Normalized BP-based）和偏移置信传播算法（Offset BP-based）的外信息概率分布和演化。基于此,分别针对Normalized BP-based和Offset BP-based算法提出了广义互信息理论（Generalized Mutual Information）及其计算公式,同时提出了改进的因子自适应LDPC译码算法,在每一次译码过程中通过一维搜索,可以获得一个最佳的修正因子,该因子能够最大化广义互信息,从而获得最佳的译码性能。分析和仿真数据表明,提出的因子自适应算法比传统的算法具有更好的性能。
개소료저밀도기우교험마（LDPC）적궤충상용역마산법급기우결점,특별용밀도진화이론분석료귀일화치신전파（Normalized BP-based）화편이치신전파산법（Offset BP-based）적외신식개솔분포화연화。기우차,분별침대Normalized BP-based화Offset BP-based산법제출료엄의호신식이론（Generalized Mutual Information）급기계산공식,동시제출료개진적인자자괄응LDPC역마산법,재매일차역마과정중통과일유수색,가이획득일개최가적수정인자,해인자능구최대화엄의호신식,종이획득최가적역마성능。분석화방진수거표명,제출적인자자괄응산법비전통적산법구유경호적성능。
This paper first presents several commonly-used decoding algorithms for the well-know low-density-parity-check（LDPC） code,then analyzes its pros and cons. The density evolution theory is employed to trace the probability density function of the extrinsic information in the iterative decoding for the Normalized BP-based and Offset BP-based algorithms. This paper further describes the concept of generalized mutual information and proposes two formulas for N-BP and O-BP-based algorithms respectively. And upon this,a coefficient-adaptive decoding algorithm is proposed. In each decoding iteration,the corresponding optimal correction coefficient can be obtained via one dimensional global search,this could also maximize the generalized mutual information,i.e.,guarantee the best decoding performance. Analysis and simulation results indicate that the proposed coefficient-adaptive algorithm is better than the traditional algorithms.