混沌通信中时变信道的盲辨识

Blind identification of time-varying channel in chaotic communication

利用混沌信号的有界性,集员广义LMS(SM-GLMS)算法成功用于混沌通信中的盲信道辨识。进一步将其应用于时变信道的盲辨识,分析了该算法的收敛性能,并提出了一种新的基于时变衰减因子的GLMS(VFF-GLMS)算法。结果表明VFF-GLMS和SM-GLMS算法性能相当,并且比常规的GLMS算法具有更好的稳态性能和时变参数跟踪能力。

With the boundedness of chaotic signal the Set-Membership Generalized Least Mean Square（SM-GLMS） algorithm has been successfully used to blind channel equalization in chaotic communications.In this paper,the SM-GLMS is further applied to time-varying channel identification and its convergence property is also justified,finally a novel GLMS algorithm with Variable Forgetting Factor（VFF-GLMS） is proposed.Simulation results show that the performance of VFF-GLMS is comparable with that of SM-GLMS.In comparison with conventional GLMS algorithm both of them provide significant improvement in terms of steady state performance and tracking ability to the variation of channel parameters.