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计算维特比正向阶段的分支度量

[英]Calculating branch metric in Viterbi forward phase

I have been looking at some implementations of Viterbi decoders and noticed a strange way of calculating the branch metric ( 1 ): 我一直在研究Viterbi解码器的一些实现,并注意到一种计算分支度量( 1 )的奇怪方法:

"BM is calculated by the product of information codewords c and hamming distance hd with R inputs." “ BM是由信息代码字c和汉明距离hd与R输入的乘积计算得出的。”

The input to the function that does the forward phase are the input bits c and also the hamming distances hd. 进行正向相位的函数的输入是输入位c,还有汉明距离hd。 My question is, how can the hamming distances be the input when the branch metric should in my understanding be the hamming distance of the input bits and the branch values of the trellis? 我的问题是,当我认为分支度量应为输入位的汉明距离和网格的分支值时,汉明距离如何输入? So why would you calculate it as the product of input bits and hamming distances, when the branch metric should actually be the hamming distance? 那么,当分支度量标准实际上应该是汉明距离时,为什么还要将其计算为输入位与汉明距离的乘积呢?

Since I didn't get any answers on this topic, and I was stuck on this for a long time, I decided to assume that the author named his variables very inaccurately. 由于我在该主题上没有得到任何答案,并且我坚持了很长时间,因此我决定假定作者对他的变量的命名非常不准确。 I believe he meant the hamming distance to actually be the branch value. 我相信他的意思是汉明距离实际上是分支价值。 What also led me to be more sure about this assumption is the following quote from the same paper: 同样使我对这一假设更加确定的是同一篇论文的以下引文:

"Because the hamming distance hd is fixed for a convolutional code, we store hd in the constant memory, which cannot be modified" “因为汉明距离hd对于卷积码是固定的,所以我们将hd存储在常量内存中,无法对其进行修改”

The hamming distance can in no way be fixed, but the branch value for the trellis structure of a convolutional code is, in fact, fixed. 汉明距离决不是固定的,但是卷积码的网格结构的分支值实际上是固定的。

To conclude, the branch metric for a hard-decision Viterbi decoder IS calculated as the hamming distance of the input code word (for a particular time stage) and the branch value of the transition it is being calculated for. 总而言之,用于硬决策维特比解码器的分支度量IS被计算为输入代码字的汉明距离(对于特定时间段),以及其转换的分支值。 The input to the branch metric calculation function are therefore NOT REALLY the hamming distances, but the fixed branch values. 因此,分支度量计算功能的输入不是真正的汉明距离,而是固定的分支值。

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