Reweighted l1-norm penalized LMS for sparse channel estimation and its analysis

Taheri, Omid; Vorobyov, Sergiy A.

doi:10.1016/j.sigpro.2014.03.048

Cited by 57 publications

(36 citation statements)

References 25 publications

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“…From the definition of g(n) for ZALMP and RZALMP, we note that the vector E[g(∞)] is bounded between −1 and 1 and similar demonstration can be read in [7,26] …”

Section: Mean Performancementioning

confidence: 90%

Sparse least mean p-power algorithms for channel estimation in the presence of impulsive noise

Chen

et al. 2015

SIViP

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The least mean p-power (LMP) is one of the most popular adaptive filtering algorithms. With a proper p value, the LMP can outperform the traditional least mean square ( p = 2), especially under the impulsive noise environments. In sparse channel estimation, the unknown channel may have a sparse impulsive (or frequency) response. In this paper, our goal is to develop new LMP algorithms that can adapt to the underlying sparsity and achieve better performance in impulsive noise environments. Particularly, the correntropy induced metric (CIM) as an excellent approximator of the l 0 -norm can be used as a sparsity penalty term. The proposed sparsity-aware LMP algorithms include the l 1 -norm, reweighted l 1 -norm and CIM penalized LMP algorithms, which are denoted as ZALMP, RZALMP and CIMLMP respectively. The mean and mean square convergence of these algorithms are analysed. Simulation results show that the proposed new algorithms perform well in sparse channel estimation under impulsive noise environments. In particular, the CIMLMP with suitable kernel width will outperform other algorithms significantly due to the superiority of the CIM approximator for the l 0 -norm. B Wentao Ma

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“…From the definition of g(n) for ZALMP and RZALMP, we note that the vector E[g(∞)] is bounded between −1 and 1 and similar demonstration can be read in [7,26] …”

Section: Mean Performancementioning

confidence: 90%

Sparse least mean p-power algorithms for channel estimation in the presence of impulsive noise

Chen

et al. 2015

SIViP

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“…On the basis of the previously reported sparse-aware LMS and AP algorithms [6][7][8][9][11][12][13]15,21,22,24], a p-norm penalty is adopted and incorporated into the cost function J AP (n) of the conventional AP algorithm, which is denoted as p-norm penalized AP (PAP) algorithm and is described as follows…”

Section: Proposed Sparse Ap Algorithmsmentioning

confidence: 99%

“…After that, zero-attracting techniques have been introduced into the proportionate adaptive filter algorithms [5,16,18], leaky least mean square [19] and normalized least mean square algorithms [7] to form desired zero attractors [6,11,17]. Their convergence characteristics are analyzed in [10,22]. Recently, a smooth approximation l 0 -norm method has been introduced into the cost function of the conventional LMS and AP algorithms to further improve the estimation performance, and these are known as l 0 -LMS and l 0 -AP algorithms [8,9,12,13].…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity Non-Uniform Penalized Affine Projection Algorithm for Sparse System Identification

Zhang

Wang

2015

Circuits Syst Signal Process

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In this paper, an improved sparse-aware affine projection (AP) algorithm for sparse system identification is proposed and investigated. The proposed sparse AP algorithm is realized by integrating a non-uniform norm constraint into the cost function of the conventional AP algorithm, which can provide a zero attracting on the filter coefficients according to the value of each filter coefficient. Low complexity is obtained by using a linear function instead of the reweighting term in the modified AP algorithm to further improve the performance of the proposed sparse AP algorithm. The simulation results demonstrate that the proposed sparse AP algorithm outperforms the conventional AP and previously reported sparse-aware AP algorithms in terms of both convergence speed and steady-state error when the system is sparse.

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“…For sparse models, the ZA-LMS achieves better steady-state performance than the standard LMS. Motivated by reweighting in compressive sampling, the RZA-LMS was proposed [6] [17]. Besides, NNCLMS [7] and DWZANLMS [23] were given to further improve the performance of l 1 -norm constraint LMS, respectively.…”

Section: Introductionmentioning

confidence: 99%

New Variable Step-size of l1-LMS Algorithm

Guan

Zhang

2016

Proceedings of the 8th International Conference on Signal Processing Systems

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In order to quickenthe convergence rateand enhanceperformanceforanti-noise when identify the unknown coefficients of a sparse system. A variable step-size l 1 -LMS algorithm is proposed. In this paper, using current error and correlation value of errorto adjust step-size and zero-attracting part. Moreover, when the current error satisfies different conditions, the two step-size formulas will switch dynamically. Some l 1 -LMS algorithms availableadapted fixed step-size. However, fixed step-size cannot balance the contradictions convergence rate and the steady-state mean square error (MSE), efficiently.In addition, how to improve performance for noise resistance may be still a problem even with correlated inputs.All are the problem needs to be studied urgently. In order to solve theabovedifficulties,an improvedvariable step-size l 1 -LMS algorithm is proposed. Performances of the proposed l 1 -LMS algorithm are analyzed.After theoretical analysis,the algorithm, experiments with different signal-to-noiseratio (SNR) are given to show the efficiency of the proposed l 1 -LMS algorithm. The variable step-size l 1 -LMS algorithm can achieve faster convergence rate, and implacable anti-noise, and identify the unknown coefficients efficiently.

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Reweighted l1-norm penalized LMS for sparse channel estimation and its analysis

Cited by 57 publications

References 25 publications

Sparse least mean p-power algorithms for channel estimation in the presence of impulsive noise

Sparse least mean p-power algorithms for channel estimation in the presence of impulsive noise

Low-Complexity Non-Uniform Penalized Affine Projection Algorithm for Sparse System Identification

New Variable Step-size of l1-LMS Algorithm

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