CAJ | 학술논문

传统咬尾码最大似然(ML)译码算法在译码时存在两个问题:复杂度高和消耗存储空间大.针对这两个问题,该文提出了一种基于Viterbi算法和双向搜索算法的最大似然译码算法.新算法利用Viterbi算法得到的幸存路径度量值与最大似然咬尾路径度量值的关系,删除不可能的起始状态及其对应的咬尾格形子图,缩小搜索空间;然后利用双向搜索算法中门限值与最大似然咬尾路径度量值的关系来降低双向搜索算法的复杂度,从而得到一种在咬尾格形图上高效率的最大似然译码算法.新的最大似然译码算法不仅降低了译码复杂度,同时降低了译码器对存储空间的需求.
전통교미마최대사연(ML)역마산법재역마시존재량개문제:복잡도고화소모존저공간대.침대저량개문제,해문제출료일충기우Viterbi산법화쌍향수색산법적최대사연역마산법.신산법이용Viterbi산법득도적행존로경도량치여최대사연교미로경도량치적관계,산제불가능적기시상태급기대응적교미격형자도,축소수색공간;연후이용쌍향수색산법중문한치여최대사연교미로경도량치적관계래강저쌍향수색산법적복잡도,종이득도일충재교미격형도상고효솔적최대사연역마산법.신적최대사연역마산법불부강저료역마복잡도,동시강저료역마기대존저공간적수구.
@@@@There exist two problems with the conventional Maximal Likelihood (ML) decoding algorithms: high decoding complexity and large memory space consumption. To solve these problems, a new algorithm that is based on Viterbi and bidirectional searching algorithm is proposed. By comparing the accumulated path metrics of survived paths with the path metric of ML tail-biting path, all of which are obtained in the Viterbi searching phase, the new algorithm deletes impossible starting states and their corresponding sub-tail-biting trellises to reduce the searching space for the second phase. In the second phase, the decoding complexity can be further reduced by comparing the path metric of ML tail-biting path with the threshold used in the bidirectional searching algorithm. Combing the Viterbi algorithm and bidirectional searching algorithm, a new ML decoding algorithm for tail-biting codes, which can be performed on tail-biting trellis with high efficiency, is obtained. The results of experiments show that the new algorithm improves the decoding efficiency and reduces the memory space consumption.