Block network error control codes and syndrome-based maximum likelihood decoding
Block network error control codes and syndrome-based maximum likelihood decoding
复制标题
块网络错误控制码和基于校正子的最大似然解码
DOI：
10.1109/isit.2008.4595098
复制
发表时间：
2008
期刊：
影响因子：
--
通讯作者：
F. Lahouti
中科院分区：
文献类型：
--
作者：
H. Bahramgiri;F. Lahouti
提出了分组网络差错控制编码(BNEC)来对抗有向无环网络中组播的差错和擦除。为了降低译码复杂度，引入了基于BNEC校正子的译码和检测算法。接下来，我们提出了一种基于校正子的BNEC译码的三阶段算法，包括错误检测、错误位置和错误值的查找。除了考虑直到精化Singleton界的有限距离译码外，我们还提出了BNEC完全译码，并证明了在足够大的域大小下，接收器t的冗余阶为deltat的码能够以接近1的概率纠正deltat-1错误。此外，还提出了完全最大似然BNEC译码。由于不同网络边缘的差错概率一般不相等，因此，在Singleton界中估计的边缘差错数量不足以用于ML译码。
The block network error control coding, BNEC, is presented to combat error and erasure for multicast in directed acyclic networks. Aiming at reducing complexity, BNEC syndrome-based decoding and detection is introduced. Next, we propose a three-stage syndrome-based BNEC decoding, comprising error detection, finding error positions and error values. Besides considering bounded-distance decoding for error correction up to refined Singleton bound, we present BNEC complete decoding and show that, a code with redundancy order deltat for receiver t, corrects deltat-1 errors with a probability approaching 1, for a sufficiently large field size. Also, complete maximum likelihood BNEC decoding is proposed. As probability of error in different network edges is not equal in general, the number of edge errors, assessed in Singleton bound, is not a sufficient statistic for ML decoding.