An analysis of the orthogonality structures of convolutional codes for iterative decoding
An analysis of the orthogonality structures of convolutional codes for iterative decoding
复制标题
迭代译码卷积码正交结构分析
DOI：
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发表时间：
2005
影响因子：
2.5
通讯作者：
D. Haccoun
中科院分区：
文献类型：
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作者：
Yucheng He;D. Haccoun
基于差分集和计算树，分析了置信传播和阈值迭代译码算法的卷积自正交码和卷积自双正交码的结构。结果表明，卷积自双正交码的双正交性通过最大化两次连续解码迭代中的独立观测值的数量同时最小化码图上长度为 6 和 8 的循环数来改进码结构。因此，双正交性可以提高迭代解码的收敛速度和错误性能。此外，双正交性使得计算树严格平衡。这允许确定最佳权重技术，使得迭代阈值解码算法的误差性能接近迭代置信传播解码算法的误差性能，但实现复杂度显着降低。
The structures of convolutional self-orthogonal codes and convolutional self-doubly-orthogonal codes for both belief propagation and threshold iterative decoding algorithms are analyzed on the basis of difference sets and computation tree. It is shown that the double orthogonality property of convolutional self-doubly-orthogonal codes improves the code structure by maximizing the number of independent observations over two successive decoding iterations while minimizing the number of cycles of lengths 6 and 8 on the code graphs. Thus, the double orthogonality may improve the iterative decoding in both convergence speed and error performance. In addition, the double orthogonality makes the computation tree rigorously balanced. This allows the determination of the best weighing technique, so that the error performance of the iterative threshold decoding algorithm approaches that of the iterative belief propagation decoding algorithm, but at a substantial reduction of the implementation complexity.