%0 Journal Article
%T Joint maximum likelihood and Bayesian channel estimation<br>联合最大似然贝叶斯信道估计
%A SHEN Bi-chuan
%A ZHENG Jian-hong
%A SHEN Min
%A <br>沈壁川
%A 郑建宏
%A 申敏
%J 重庆邮电大学学报(自然科学版)
%D 2008
%I 
%X Statistical Bayesian channel estimation is effective in suppressing noise floor for high SNR, but its performance degrades due to less reliable noise estimation in low SNR region. Based on a robust nonlinear de-noising technique for small signal, a simplified joint maximum likelihood and Bayesian channel estimation is proposed and investigated. Simulation results are presented and analysis shows it is promising to improve channel estimation and joint detection performance for both low and high SNR situations.
%K maximum likelihood estimation
%K bayesian channel estimation
%K noise floor
%K Teager-Kaiser filter<br>最大似然方法
%K 贝叶斯信道估计
%K 噪底
%K Teager-Kaiser滤波器
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=01BA20E8BA813E1908F3698710BBFEFEE816345F465FEBA5&cid=96E6E851B5104576C2DD9FC1FBCB69EF&jid=5C2694A2E5629ECD6B59D7B28C6937AD&aid=13F80C4F6EB1B3970F6CC81FDABCC9CD&yid=67289AFF6305E306&vid=A04140E723CB732E&iid=94C357A881DFC066&sid=6313C162FF75889A&eid=E5D85F291CED2DA6&journal_id=1673-825X&journal_name=重庆邮电大学学报(自然科学版)&referenced_num=0&reference_num=9