中国物理B ›› 2007, Vol. 16 ›› Issue (6): 1619-1623.doi: 10.1088/1009-1963/16/6/022
赵知劲, 郑仕链, 徐春云, 孔宪正
Zhao Zhi-Jin(赵知劲)†, Zheng Shi-Lian(郑仕链), Xu Chun-Yun(徐春云), and Kong Xian-Zheng(孔宪正)
摘要: Hidden Markov models (HMMs) have been used to model burst error sources of wireless channels. This paper proposes a hybrid method of using genetic algorithm (GA) and simulated annealing (SA) to train HMM for discrete channel modelling. The proposed method is compared with pure GA, and experimental results show that the HMMs trained by the hybrid method can better describe the error sequences due to SA's ability of facilitating hill-climbing at the later stage of the search. The burst error statistics of the HMMs trained by the proposed method and the corresponding error sequences are also presented to validate the proposed method.
中图分类号: (Numerical optimization)