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Reduced-Complexity Decoder Architecture for Non-Binary LDPC Codes

机译:非二进制LDPC码的降低复杂度的解码器架构

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Non-binary low-density parity-check (NB-LDPC) codes can achieve better error-correcting performance than binary LDPC codes when the code length is moderate at the cost of higher decoding complexity. The high complexity is mainly caused by the complicated computations in the check node processing and the large memory requirement. In this paper, a novel check node processing scheme and corresponding VLSI architectures are proposed for the Min-max NB-LDPC decoding algorithm. The proposed scheme first sorts out a limited number of the most reliable variable-to-check (v-to-c) messages, then the check-to-variable (c-to-v) messages to all connected variable nodes are derived independently from the sorted messages without noticeable performance loss. Compared to the previous iterative forward-backward check node processing, the proposed scheme not only significantly reduced the computation complexity, but eliminated the memory required for storing the intermediate messages generated from the forward and backward processes. Inspired by this novel c-to-v message computation method, we propose to store the most reliable v-to-c messages as “compressed” c-to-v messages. The c-to-v messages will be recovered from the compressed format when needed. Accordingly, the memory requirement of the overall decoder can be substantially reduced. Compared to the previous Min-max decoder architecture, the proposed design for a (837, 726) code over $GF(2^{5})$ can achieve the same throughput with only 46% of the area.
机译:当代码长度适中时,非二进制低密度奇偶校验(NB-LDPC)码比二进制LDPC码可以获得更好的纠错性能,但代价是解码复杂度更高。高复杂度主要是由于校验节点处理中的计算复杂以及内存需求大所致。本文针对Min-max NB-LDPC解码算法,提出了一种新颖的校验节点处理方案和相应的VLSI体系结构。所提出的方案首先分类出有限数量的最可靠的变量对校验(v-to-c)消息,然后独立地导出到所有连接的变量节点的校验对变量(c-to-v)消息。排序后的消息中,不会造成明显的性能损失。与先前的迭代前后检查节点处理相比,该方案不仅显着降低了计算复杂度,而且消除了存储从向前和向后过程生成的中间消息所需的内存。受这种新颖的c-to-v消息计算方法的启发,我们建议将最可靠的v-to-c消息存储为“压缩的” c-v消息。必要时将从压缩格式中恢复C-V消息。因此,可以大大减少整个解码器的存储需求。与以前的Min-max解码器体系结构相比,在 $ GF(2 ^ {5})$ 可以仅以46%的面积实现相同的吞吐量。

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