基于分析矩阵零均值比的CDL卷积交织盲识别

针对非合作接收中现有盲识别算法受码长约束且抗误码性能较差的问题,提出了一种基于分析矩阵零均值比的卷积交织参数盲识别算法,可以在不需要考虑码长约束的情况下,利用经高斯约当旋转消元算法处理后得到的分析矩阵零均值比来确定秩缺间隔,从而得到交织深度的范围,最后通过联合求取交织宽度、交织深度和交织偏差来完成卷积交织参数的盲识别。分别对所提算法、多重循环搜索算法和秩准则算法在不同误码性能下进行了仿真实验,实验结果表明,所提算法在误码率较高情况下,具有较好的识别性能,且运算复杂度较小,可以完成通用数据链卷积交织的盲识别。

Blind identification of convolutional interleaver parameters based on zero mean ratio for common data link#br#

As the existing blind recognition algorithms in non cooperative reception are constrained by code length and have poor anti error performance, a blind identification algorithm of convolutional interleaver parameters based on zero mean ratio for common data link (CDL) is proposed in high speed transmission. Unlike the prior works, the interleaver parameter identification process is carried out for a generic case without any restriction on the codeword length. Combining the zero mean ratio with Gauss Jordan elimination through the pivoting algorithm, the distance of rank deficiency can be determined and the depth is estimated in the presence of bit errors. Finally, the blind identification can be realized through the combination of depth, width and synchronization. The proposed algorithm, nested loop algorithm and rank criterion algorithm are used for simulation tests under different bit error rates. The experimental results show that the proposed method can achieve a better performance compared with the existing algorithms in high bit error rate and can realize the identification of convolutional interleaver parameters for CDL.