Abstract 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.
Received: 27 August 2006
Revised: 27 October 2006
Accepted manuscript online:
Fund: Project supported by
Pre-Research Foundation of Electronics Science Research Institute
(Grant No 41101040102).
Cite this article:
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) Discrete channel modelling based on genetic algorithm and simulated annealing for training hidden Markov model 2007 Chinese Physics 16 1619
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