基于最小和算法的改进LDPC译码算法

Yue Cao
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引用次数: 9

摘要

低密度奇偶校验码是一种高性能的线性分组码。它具有接近香农极限的优良性能。LDPC具有解码复杂度低、结构自由等优点,受到广泛关注。LDPC的典型译码算法是LLR BP算法,也称为“和积算法”。LLR BP算法是目前最好的算法。但它需要复杂的计算,这给硬件设计带来了很大的困难。为了解决这一问题,提出了一种近似LLR BP算法的算法,称为最小和算法。最小和算法大大减少了计算量,简化了硬件设计,但其精度与BP算法有较大差距。分析了最小和算法存在误差的原因,在最小和算法的基础上提出了一种改进的最小和线性逼近算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An improved LDPC decoding algorithm based on min-sum algorithm
Low density parity check code is a kind of high-performance linear block code. It has excellent performance which is near the Shannon limit. LDPC has low decoding complexity, free structure and receives extensive attention. The typical decoding algorithm of LDPC is LLR BP algorithm, which is also called “sum-product algorithm”. LLR BP algorithm is the best algorithm at present. But it needs complex computation, which causes great difficulty in its hardware design. To solve this problem, an algorithm that gets approximation of LLR BP algorithm is put forward, which is called min-sum algorithm. Min-sum algorithm greatly reduces the computation and makes the hardware design simpler, but its accuracy has a wide gap with BP algorithm. This article analyzes the reason why min-sum algorithm has errors, and puts forward an improved algorithm called min-sum linear approximation algorithm based on min-sum algorithm.
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