基于二元译码信息的迭代大数逻辑LDPC译码算法及其量化优化

该文提出一种低复杂度的迭代大数逻辑LDPC译码算法，在迭代过程中所有的译码信息都以二元形式进行传递、处理和迭代更新。所提算法不需要计算外信息，而是利用Tanner图上伴随式的对错状态来评判节点可靠度。与现有的几种迭代大数逻辑译码算法相比，该文算法也不需要信息修正处理，避免了相应的实数乘法操作，具有很低的译码复杂度。此外，该文引入一种特殊的量化处理函数，并给出了基于离散密度进化的参数优化过程。实验仿真表明，该文所提算法与原算法相比，在AWGN信道下可获得约0.3~0.4 dB的性能提升。同时，由于节点间交换传递的译码信息都是基于1个比特位的二元信息，也非常便于硬件的设计与实现。

Binary Decoding Message Iterative Majority-logic LDPC Decoding and Its Quantizing Optimization

A low complexity iterative majority-logic decoding algorithm is presented. For the presented algorithm, binary decoding messages are involved in the message passing, processing and updating process. Instead of computing the extrinsic information, the presented algorithm computes the reliability measure based on syndrome states (correct or error) in the Tanner graph. Compared with several existing iterative majority-logic decoding algorithms, the presented algorithm does not require the information scaling and hence can avoid the corresponding real multiplication operations. This leads to very low decoding complexity. Furthermore, a special quantization is combined with the presented algorithm. The optimization method is also given based on the discrete Density Evolution (DE). Simulation results show that, compared with the original algorithm, the presented algorithm can achieve about 0.3~0.4 dB performance gain over the Additive White Gaussian Noise (AWGN) channel. Moreover, all the decoding messages exchanged among the nodes are binary-based, which makes the presented algorithm convenient for the hardware implementations.