Abstract: Aiming at the problem of convergence to local minima in traditional blind equalization algorithm, a new blind equalization algorithm based on artificial immune network is presented. The coefficient vector of a blind equalizer is regarded as the antibodies and the optimal solutions can be obtained by replication, mutation and suppression. Simulation experimental results show this algorithm is effective.

Key words: artificial immune network, blind equalization, immune optimization

摘要： 针对传统常模盲均衡算法存在的收敛到局部极小值点问题，提出一种基于人工免疫网络的盲均衡算法，把均衡器系数向量作为抗体，经过一系列抗体克隆、变异和抑制等操作，搜索到适应度值最高的抗体，即均衡器的最优系数。仿真实验结果表明，该算法是有效的。

关键词: 人工免疫网络, 盲均衡, 免疫优化

CLC Number: 

• TP391