基于可信位置排序的咬尾卷积码译码算法

咬尾卷积码的传统译码算法没有考虑咬尾格形图的循环性，译码起始位置固定，译码效率相对较低。该文首次证明了咬尾卷积码基于格形图的译码算法与译码起始位置无关，即从任意位置开始译码得到的最优咬尾路径即为全局最优咬尾路径。基于此提出一种基于可信位置排序的咬尾卷积码译码算法。新算法利用咬尾格形图的循环性，根据接收到的信道输出序列估算每个译码起始位置的可靠性，从而选择一个可靠性最高的译码起始位置。和传统译码算法相比，所提算法具有更快的收敛速度。

Belief Ranking Based Low-complexity Maximum Likelihood Decoding Algorithm for Tail-biting Convolutional Codes

For a long time, the circularity of the tail-biting trellis is ignored in conventional decoding algorithms of Tail-Biting Convolutional Codes (TBCC). This kind of algorithm starts decoding from the fixed location, and consequently exhibits relatively lower decoding efficiency. For the first time, this paper proves that the decoding result of the tail-biting convolutional codes is independent on the decoding starting location. It means that the Maximum Likelihood (ML) tail-biting path, which starts from any location of the tail-biting trellises, is the global ML tail-biting path. Based on this observation, a new ML decoding algorithm is proposed. The new algorithm ranks the belief-value of each location on the trellis at first, and then selects the location with the highest belief- value as the decoding starting location. Compared with other existing ML decoders, the new decoder exhibits higher convergence speed.