倒谱与相位模糊条件下的卷积码高容错识别

Robust Recognition of Convolutional Codes With Cepstrum and Phase Ambiguity

针对倒谱、相位模糊及误码存在条件下的不同码率卷积码识别问题,本文提出了一种容错能力较强的识别算法。以(133,171)卷积码为例,在QPSK调制方式下,首先推导出符号信息到比特软信息的转换关系,然后利用本文提出的校验向量求解算法得到各个条件下的校验向量,最后利用Walsh-Hadamard变换(WHT)、对数似然比(LLR)、似然差(LD)三种方法对校验向量的识别性能进行了测试。仿真结果表明,对于(133,171)卷积码,利用求得的校验向量能够在较低信噪比下有效识别各种码率、相位模糊以及倒谱,且将分析范围从56种情况减少至2种或4种情况,计算复杂度较低,能够适应实际环境需求。

It is a little difficult to recognize convolutional codes with different code rates when cepstrum,phase ambiguity and bit errors exist. A strong fault tolerance algorithm is proposed in this paper to solve this problem. This paper takes（ 133,171） convolutional codes for example under the QPSK modulation mode. Firstly,the transformation relationship between symbol information and bit soft information is derived. Then,check vectors under various conditions are obtained by using the check vector solution algorithm proposed in this paper. Finally,performance of check vectors is tested by using three methods which include Walsh-Hadamard transform（ WHT）,log-likelihood ratio（ LLR） and likelihood difference（ LD）. Simulation results show that for（ 133,171） convolutional codes,we can identify different code rates,degree phase ambiguity and cepstrum effectively at low snr by using check vectors. Moreover,the algorithm reduces the analysis range from 56 to 2 or 4,has low computational complexity and it can meet the needs of the actual environment.