论文部分内容阅读
针对目前递归系统卷积(RSC)码盲识别算法容错性差、计算量大的问题,提出了基于遗传算法的RSC多项式参数盲识别算法。首先根据RSC码特殊的编码结构,构建了基于遗传算法的识别模型,将结果向量的码重作为适应度函数,然后推导出了不同误码率条件下平均码重的理论值,实现了算法中最优门限的获得。该算法容错性能较好,并且最大计算量只与初始种群的规模、遗传代数的上限以及输出路数成正比。最后仿真验证表明,理论推导的码重分布情况能够与仿真结果较好地吻合,并且在误码率高达0.06的情况下,各种寄存器个数下的RSC码参数识别率接近于0.9。
Aiming at the problem of poor fault tolerance and large amount of computation of the current blind convolutional (RSC) code recognition algorithm, a RSC polynomial blind parameter identification algorithm based on genetic algorithm is proposed. Firstly, based on the special coding structure of RSC code, a recognition model based on genetic algorithm is constructed. The code weight of the result vector is taken as the fitness function, and then the theoretical value of the average code weight under different bit error rates is deduced. Get the optimal threshold. The algorithm has better fault-tolerant performance, and the maximum computational cost is only proportional to the size of the initial population, the upper limit of the genetic algebra and the number of outputs. Finally, the simulation results show that the code deduced from the theoretical model can be well fitted with the simulation results, and the RSC code recognition rate of various registers is close to 0.9 with bit error rate up to 0.06.