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

通信学报 ›› 2014, Vol. 35 ›› Issue (7): 172-177.doi: 10.3969/j.issn.1000-436x.2014.07.021

引入梯度导引似p范数约束的稀疏信道估计算法

伍飞云,周跃海,童峰

厦门大学水声通信与海洋信息技术教育部重点实验室，福建厦门 361005

出版日期:2014-07-25 发布日期:2017-06-24
基金资助:
国家自然科学基金资助项目;教育部高等学校博士点专项基金资助项目

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

摘要/Abstract

摘要：

为克服l₀和l₁范数约束的最小均方算法在不同信道稀疏程度下对稀疏信道估计中出现的收敛性能起伏较大等缺点，提出一种新的似p范数约束的最小均方算法，通过在最小均方算法代价函数中引入p值可变的似p范数约束以适应信道的不同稀疏程度，并在验证代价函数凸性的基础上导出p值的梯度导引寻优。最后给出仿真实验及其讨论，实验结果表明了新算法的优越性。

关键词: 似p范数约束, 最小均方算法, 稀疏信道

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

伍飞云,周跃海,童峰. 引入梯度导引似p范数约束的稀疏信道估计算法[J]. 通信学报, 2014, 35(7): 172-177.

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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参考文献 14

[1]	COTTER S F , RAO B D . Sparse channel estimation via matching pursuit with application to equalization[J]. IEEE Trans Commun, 2002,50(3):374-377.
[2]	NAYLOR P A , CUI J , BROOKES M . Adaptive algorithms for sparse echo cancellation[J]. Signal Processing, 2006,86(6):1182-1192.
[3]	GU Y , JIN J , MEI S . L0norm constraint LMS algorithm for sparse system identification[J]. IEEE Signal Processing Letters, 2009,16(9):774-777.
[4]	CHEN Y , GU Y , HERO A O . Sparse LMS for system identification[A]. Int Conf Acoust, Speech, Signal Process (ICASSP)[C]. Taiwan, China, 2009. 3125-3128.
[5]	JIN J , GU Y , MEI S . A stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework[J]. IEEE J Sel Topics Signal Process, 2010,4(2):409-420.
[6]	SHI K , SHI P . Adaptive sparse Volterra system identification with l0-norm penalty[J]. Signal Processing, 2011,91(10):2432-2436.
[7]	SHI K , SHI P . Convergence analysis of sparse LMS algorithms with l1-norm penalty based on white input signal[J]. Signal Processing, 2010,90(12):3289-3293.
[8]	RAO B D , DELGADO K K . An affine scaling methodology for best basis selection[J]. IEEE Trans Signal Process, 1999,47(1):187-200.
[9]	SCHREIBER W F . Advanced television systems for terrestrial broad-casting: some problems and some proposed solutions[J]. Proc IEEE, 1995,83:958-981.
[10]	KALOUPTSIDIS N , MILEOUNIS G , BABADI B , et al. Adaptive algorithms for sparse system identification[J]. Signal Processing, 2011,91(8):1910-1919.
[11]	ANGELOSANTE D , BAZERQUE J A , GIANNAKIS G B . Online adaptive estimation of sparse signals: where RLS meets the l1-norm[J]. IEEE Trans, Signal Process, 2010,58(7):3436-3447.
[12]	GORODNITSKY I F , RAO B D . A recursive weighted mini-mum-norm algorithm: analysis and applications[A]. Proc CASSP[C]. Minneapolis, MN, 1993. 456-459.
[13]	HARIKUMAR G , BRESLER Y . A new algorithm for computing sparse solutions to linear inverse problems[A]. Proc ICASSP[C]. Atlanta, GA, 1996. 1331-1334.
[14]	CABALLERO I S , PRIETO C J P , RODRIGUEZ A A . Sparse decon-volution using adaptive mixed-Gaussian models[J]. Signal Process, 1996,54(2):161-172.

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

引入梯度导引似p范数约束的稀疏信道估计算法

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

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