Mixed Norm Constrained Sparse APA Algorithm for Satellite and Network Echo Channel Estimation

Li, Yingsong; Jiang, Zhengxiong; Osman, Omer Mohammed Omer; Han, Xiao; Yin, Jingwei

doi:10.1109/access.2018.2878310

Cited by 47 publications

(20 citation statements)

References 26 publications

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“…To remit the negative influence of heavy-colored input data on the convergence performance [5], derived the affine projection algorithm (APA), which is based on the idea of affine subspace projection. After that, the family of APA is developed by various methods [5][6][7][8][9][10][11][12][13][14][15][16]. Rather than a single current data vector used in LMS and NLMS, APA updates weight coefficients through multiple, most recent input vectors.…”

Section: Introductionmentioning

confidence: 99%

Modified Combined-Step-Size Affine Projection Sign Algorithms for Robust Adaptive Filtering in Impulsive Interference Environments

Zhang

Zhao

2020

Symmetry

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In this paper, to further improve the filtering performance and enhance the poor tracking capability of the conventional combined step-size affine projection sign algorithm (CSS-APSA) in system identification, we propose a simplified CSS-APSA (SCSS-APSA) by applying the first-order Taylor series expansion to the sigmoidal active function (of which the independent variable is symmetric) of CSS-APSA. SCSS-APSA has lower computational complexity, and can achieve comparable, or even better filtering performance than that of CSS-APSA. In addition, we propose a modification of the sigmoidal active function. The modified sigmoidal active function is a form of scaling transformation based on the conventional one. Applying the modified function to the CSS-APSA, we can obtain the modified CSS-APSA (MCSS-APSA). Moreover, the extra parameter of MCSS-APSA provides the power to accelerate the convergence rate of CSS-APSA. Following the simplification operations of SCSS-APSA, the computational complexity of MCSS-APSA can also be reduced. Therefore, we get the simplified MCSS-APSA (SMCSS-APSA). Simulation results demonstrate that our proposed algorithms are able to achieve a faster convergence speed in system identification.

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Section: Introductionmentioning

confidence: 99%

Modified Combined-Step-Size Affine Projection Sign Algorithms for Robust Adaptive Filtering in Impulsive Interference Environments

Zhang

Zhao

2020

Symmetry

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“…For example, the proportionate NLMS (PNLMS) combines the proportionate scheme into the NLMS to reassign the gains to each channel coefficients [26]. Then, proportionate-type AF algorithms were widely realized and utilized for channel estimation as well as the echo cancellation [27][28][29][30]. For the sake of comparison with the traditional NLMS, the PNLMS suffers from slow convergence if the input signal is driven by colored or speech signals, resulting in that steady-state error might be worse than that of the NLMS.…”

Section: Introductionmentioning

confidence: 99%

A Correntropy-Based Proportionate Affine Projection Algorithm for Estimating Sparse Channels with Impulsive Noise

Jiang

Huang

2019

Entropy

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A novel robust proportionate affine projection (AP) algorithm is devised for estimating sparse channels, which often occur in network echo and wireless communication channels. The newly proposed algorithm is realized by using the maximum correntropy criterion (MCC) and the data reusing scheme used in AP to overcome the identification performance degradation of the traditional PAP algorithm in impulsive noise environments. The proposed algorithm is referred to as the proportionate affine projection maximum correntropy criterion (PAPMCC) algorithm, which is derived in the context of channel estimation framework. Many simulation results were obtained to verify that the PAPMCC algorithm is superior to early reported AP algorithms with different input signals under impulsive noise environments.

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“…According to the active coefficient distribution in these sparse systems, the sparse systems are divided into three types [18][19][20][21][22][23]: (1) General sparse systems; (2) one-group sparse systems, and (3) multi-group sparse systems. It is known to us that the network echo paths are modeled as a typical one-group system while the satellite link echo paths have been modeled as multi-group systems, owing to the bulk delays which are always extant in network encoding and jitter buffer delays [19,21].…”

Section: Introductionmentioning

confidence: 99%

A Novel Generalized Group-Sparse Mixture Adaptive Filtering Algorithm

Cherednichenko

Jiang

et al. 2019

Symmetry

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A novel adaptive filtering (AF) algorithm is proposed for group-sparse system identifications. In the devised algorithm, a novel mixed error criterion (MEC) with two-order logarithm error, p-order errors and group sparse constraint method is devised to give a resistant to the impulsive noise. The proposed group-sparse MEC can fully use the known group-sparse characteristics in the cluster sparse systems, and it is derived and analyzed in detail. Various simulations are presented and analyzed to give a verification on the effectiveness of the developed group-sparse MEC algorithms, and the simulated results shown that the developed algorithm outperforms the previously developed sparse AF algorithms for identifying the systems.

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Mixed Norm Constrained Sparse APA Algorithm for Satellite and Network Echo Channel Estimation

Cited by 47 publications

References 26 publications

Modified Combined-Step-Size Affine Projection Sign Algorithms for Robust Adaptive Filtering in Impulsive Interference Environments

Modified Combined-Step-Size Affine Projection Sign Algorithms for Robust Adaptive Filtering in Impulsive Interference Environments

A Correntropy-Based Proportionate Affine Projection Algorithm for Estimating Sparse Channels with Impulsive Noise

A Novel Generalized Group-Sparse Mixture Adaptive Filtering Algorithm

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