引入梯度导引似p范数约束的稀疏信道估计算法

doi:10.3969/j.issn.1000-436x.2014.07.021

Journal on Communications ›› 2014, Vol. 35 ›› Issue (7): 172-177.doi: 10.3969/j.issn.1000-436x.2014.07.021

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Estimation algorithm for sparse channels with gradient guided p-norm like constraints

Fei-yun WU,Yue-hai ZHOU,Feng TONG

Key Laboratory of Underwater Acoustic Communication and Marine Information Technique of the Ministry of Education, Xiamen University, Xiamen 361005, China

Online:2014-07-25 Published:2017-06-24
Supported by:
The National Natural Science Foundation of China;The Specialized Research Fund for the Doctoral Program of Higher Education of China

Abstract

Abstract:

The l₀and l₁norm constrained least mean square (LMS) algorithm can effectively improve the performance of the sparse channel estimation, but the convergence performance of such algorithms will considerably vary when the channel exhibits different sparisity. A novel p-norm like constraint LMS algorithm to accommodate the various sparisity of the channels through the introducing of the variable p-value was presented. Furthermore, the gradient guided optimiza-tion of the p-value was derived. Numerical simulation results are given to demonstrate the superiority of the new algorithm.

Key words: p-norm like constraint, LMS algorithm, sparse channels

Fei-yun WU,Yue-hai ZHOU,Feng TONG. Estimation algorithm for sparse channels with gradient guided p-norm like constraints[J]. Journal on Communications, 2014, 35(7): 172-177.

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References 14

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算法	μ	κ	δ	T
LMS	0.005 2	NA	NA	NA
l₀-LMS	0.006 6	0.000 33	NA	NA
l₁-LMS	0.006 0	0.000 30	NA	NA
p-norm-like-LMS	0.013 0	0.000 65	0.01	10

算法	μ	κ	δ	T
LMS	0.004	NA	NA	NA
l₀- LMS	0.004	0.000 2	NA	NA
l₁- LMS	0.004	0.000 2	NA	NA
p-norm-like - LMS	0.004	0.000 3	0.01	10

Estimation algorithm for sparse channels with gradient guided p-norm like constraints

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