Blind Equalization Based on RLS Algorithm Using Adaptive Forgetting Factor for Underwater Acoustic Channel

Blind equalization based on adaptive forgetting factor, recursive least squares （RLS） with constant modulus algorithm （CMA）, is investigated. The cost function of CMA is simplified to meet the second norm form to ensure the stability of RLS-CMA, and thus an improved RLS-CMA （RLS-SCMA） is established...

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Bibliographic Details
Published inChina ocean engineering Vol. 28; no. 3; pp. 401 - 408
Main Author 肖瑛 殷福亮
Format Journal Article
LanguageEnglish
Published Heidelberg Chinese Ocean Engineering Society 01.06.2014
Subjects
Adaptive algorithms
Algorithms
Blinds
Channels
Coastal Sciences
Cost function
Engineering
Equalization
Errors
Fluid- and Aerodynamics
Marine
Marine & Freshwater Sciences
Numerical and Computational Physics
Oceanography
Offshore Engineering
RLS算法
Simulation
Underwater acoustics
恒模算法
成本函数
水声信道
盲均衡
自适应
递推最小二乘
遗忘因子
blind equalization
RLS
second norm form
CMA
underwater acoustic channel
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ISSN0890-5487
2191-8945
DOI10.1007/s13344-014-0032-5

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Summary:Blind equalization based on adaptive forgetting factor, recursive least squares （RLS） with constant modulus algorithm （CMA）, is investigated. The cost function of CMA is simplified to meet the second norm form to ensure the stability of RLS-CMA, and thus an improved RLS-CMA （RLS-SCMA） is established. To further improve its performance, a new adaptive forgetting factor RLS-SCMA （ARLS-SCMA） is proposed. In ARLS-SCMA, the forgetting factor varies with the output error of the blind equalizer during the iterative process, which leads to a faster convergence rate and a smaller steady-state error. The simulation results prove the effectiveness under the condition of the underwater acoustic channel.
Bibliography:32-1441/P
Blind equalization based on adaptive forgetting factor, recursive least squares （RLS） with constant modulus algorithm （CMA）, is investigated. The cost function of CMA is simplified to meet the second norm form to ensure the stability of RLS-CMA, and thus an improved RLS-CMA （RLS-SCMA） is established. To further improve its performance, a new adaptive forgetting factor RLS-SCMA （ARLS-SCMA） is proposed. In ARLS-SCMA, the forgetting factor varies with the output error of the blind equalizer during the iterative process, which leads to a faster convergence rate and a smaller steady-state error. The simulation results prove the effectiveness under the condition of the underwater acoustic channel.
blind equalization; RLS; CMA; underwater acoustic channel; second norm form
XIAO Ying , YIN Fu-liang （ a College of Information and Communication Engineering, Dalian Nationality University, Dalian 116600, China ;b Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China）
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0890-5487
2191-8945
DOI:10.1007/s13344-014-0032-5