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Construction of protographs for large-girth structured LDPC convolutional codes

机译:大尺寸结构化LDPC卷积码的原型构造

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In this paper, we present a method to construct girth-6 protographs that lead to the shortest constraint length in the convolutional structure. A stringent structural constraint is imposed on the protographs such that the decoder can be implemented efficiently. Then, given the structural constraint, it is shown that finding the aforementioned protographs is equivalent to solving a simple algebraic problem. Based on this mathematical formulation, girth-6 protographs are created without having to resort to a graph search. Using the girth-6 protographs, we derive good low-density parity-check (LDPC) convolutional codes by using periodic quasi-cyclic lifting. The performance of such constructed codes is compared with AR4JA-based LDPC convolutional codes.
机译:在本文中,我们提出了一种构造girth-6原型的方法,该方法导致卷积结构中的约束长度最短。对原型施加严格的结构约束,以便可以有效地实现解码器。然后,在给定结构约束的情况下,表明找到上述原型等同于解决简单的代数问题。基于此数学公式,无需借助图搜索即可创建第6周长的原型。使用girth-6原型,我们通过使用周期性准循环提升来导出良好的低密度奇偶校验(LDPC)卷积码。将这种构造代码的性能与基于AR4JA的LDPC卷积代码进行了比较。

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