arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1104.1471cs.ITmath.IT
收藏

提高二元线性码ML译码性能的新方法

New Techniques for Upper-Bounding the ML Decoding Performance of Binary Linear Codes

Xiao Ma, Jia Liu, Baoming Bai

摘要:

在本文中,新的技术,提出了简化或改善现有的最大似然(ML)解码性能的二进制线性码在加性高斯白噪声(AWGN)信道上的上限。首先,基于Gallager的第一边界技术(GFBT),重新推导了Ma { \em et al}最近提出的使用截断权重谱的联合边界,其中“好区域”由次优列表解码算法指定.由坏区域引起的错误概率可以由二项分布的尾概率上界,而由好区域引起的错误概率可以由大多数现有技术上界。其次,我们提出了两种技术来收紧的好区域所造成的错误概率的联合界。第一种技术是基于成对的错误概率,这可以通过采用错误事件与接收到的随机向量的某些分量之间的独立性来进一步收紧。第二种技术是基于三重错误概率,这可以通过证明任何三个双极向量形成一个非钝角三角形的上限。所提出的边界改进了传统的联合边界,但具有类似的复杂性,因为它们只涉及$Q$函数。所提出的界限也可以适应比特错误概率。

英文摘要:

In this paper, new techniques are presented to either simplify or improve most existing upper bounds on the maximum-likelihood (ML) decoding performance of the binary linear codes over additive white Gaussian noise (AWGN) channels. Firstly, the recently proposed union bound using truncated weight spectrums by Ma {\em et al} is re-derived in a detailed way based on Gallager's first bounding technique (GFBT), where the "good region" is specified by a sub-optimal list decoding algorithm. The error probability caused by the bad region can be upper-bounded by the tail-probability of a binomial distribution, while the error probability caused by the good region can be upper-bounded by most existing techniques. Secondly, we propose two techniques to tighten the union bound on the error probability caused by the good region. The first technique is based on pair-wise error probabilities, which can be further tightened by employing the independence between the error events and certain components of the received random vectors. The second technique is based on triplet-wise error probabilities, which can be upper-bounded by proving that any three bipolar vectors form a non-obtuse triangle. The proposed bounds improve the conventional union bounds but have a similar complexity since they involve only the $Q$-function. The proposed bounds can also be adapted to bit-error probabilities.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑