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题名: Norm Penalized Joint-Optimization NLMS Algorithms for Broadband Sparse Adaptive Channel Estimation
作者: Wang, Yanyan; Li, Yingsong
作者部门: 微波遥感部
通讯作者: Li, YS (reprint author), Harbin Engn Univ, Coll Informat & Commun Engn, Harbin 150001, Heilongjiang, Peoples R China. ; Li, YS (reprint author), Chinese Acad Sci, Natl Space Sci Ctr, Beijing 100190, Peoples R China.
关键词: NLMS algorithm ; least mean square (LMS) ; zero attracting ; joint-optimization ; sparse channel estimations
刊名: SYMMETRY-BASEL
ISSN号: 2073-8994
出版日期: 2017
卷号: 9, 期号:8, 页码:133
收录类别: SCI
项目资助者: National Key Research and Development Program of China-Government Corporation Special Program [2016YFE0111100] ; National Science Foundation of China [61571149] ; Science and Technology innovative Talents Foundation of Harbin [2016RAXXJ044] ; Projects for the Selected Returned Overseas Chinese Scholars of Heilongjiang Province ; Ministry of Human Resources and Social Security of the People's Republic of China (MOHRSS) ; PhD Student Research and Innovation Fund of the Fundamental Research Funds for the Central Universities [HEUGIP201707]
英文摘要: A joint-optimization method is proposed for enhancing the behavior of the l(1)-norm-and sum-log norm-penalized NLMS algorithms to meet the requirements of sparse adaptive channel estimations. The improved channel estimation algorithms are realized by using a state stable model to implement a joint-optimization problem to give a proper trade-off between the convergence and the channel estimation behavior. The joint-optimization problem is to optimize the step size and regularization parameters for minimizing the estimation bias of the channel. Numerical results achieved from a broadband sparse channel estimation are given to indicate the good behavior of the developed joint-optimized NLMS algorithms by comparison with the previously proposed l(1)-normand sum-log norm-penalized NLMS and least mean square (LMS) algorithms.
语种: 英语
内容类型: 期刊论文
URI标识: http://ir.nssc.ac.cn/handle/122/6151
Appears in Collections:微波遥感部_期刊论文

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