基于分段抽取软判决加权Walsh Hadamard变换的卷积码识别算法

针对低信噪比环境下卷积码识别研究存在的不足,提出一种基于分段抽取软判决加权Walsh Hadamard变换(WHT)的卷积码识别算法。该算法利用接收比特的解调软判决信息求取软判决频次序列,并构造加权Walsh Hadamard矩阵,从而识别得到基本校验序列。利用基本编码矩阵构造规则,最终实现记忆长度及基本生成矩阵的识别。算法结合分段抽取思想,降低了所需运行存储量。仿真实验表明,该算法可在低信噪比环境下对不同码率卷积码进行有效识别,具有较好的容错性,且对大约束度的卷积码性能提高更为显著。

An Algorithm for Convolutional Codes Recognition Based on Sectionally Extracting Soft-decision Weighted Walsh Hadamard Transform

An algorithm for convolutional code recognition based on sectionally extracting soft-decision weighted Walsh Hadamard transform(WHT) is proposed for the issue of convolutional code recognition in the low signal-to-noise ratio(SNR) environment. The proposed algorithm is used to obtain a soft-decision frequency sequence by using received bits’ soft-decision information, and construct a weighted Walsh Hadamard matrix to recognize the basic check sequence. The code memory length and basic generated matrix are recognized based on the construction rule of basic encoding matrixt. The algorithm has the reduced run storage space. Simulation results show that the algorithm could recognize the convolutional codes with different coding rates in the low SNR environment, and has good fault-tolerant performance, which is improved greatly especially in recognition of long restraint-length convolutional codes.