Diffusion Improved Multiband-Structured Subband Adaptive Filter Algorithm With Dynamic Selection of Nodes Over Distributed Networks

Abadi, Mohammad Shams Esfand; Ahmadi, Mohammad Javad

doi:10.1109/tcsii.2018.2865101

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Although GMSD in (40) does not depend on the unknowñ x, we cannot calculate it at each iteration as some expected values are needed for computing f 21 , f 3 , and f 4 . To circumvent this problem, we use the instantaneous values instead [41], [42]. Now, since the GMSD in ( 40) is a function of α(n), the optimal α(n) can be found by solving the following optimization problem:…”

Section: B Fast Elms For Graph Signalsmentioning

confidence: 99%

Efficient Estimation of Graph Signals With Adaptive Sampling

Ahmadi

Arablouei

Abdolee

2020

IEEE Trans. Signal Process.

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We propose two new least mean squares (LMS)-based algorithms for adaptive estimation of graph signals that improve the convergence speed of the LMS algorithm while preserving its low computational complexity. The first algorithm, named extended least mean squares (ELMS), extends the LMS algorithm by virtue of reusing the signal vectors of previous iterations alongside the signal available at the current iteration. Utilizing the previous signal vectors accelerates the convergence of the ELMS algorithm at the expense of higher steady-state error compared to the LMS algorithm. To further improve the performance, we propose the fast ELMS (FELMS) algorithm in which the influence of the signal vectors of previous iterations is controlled by optimizing the gradient of the mean-square deviation (GMSD). The FELMS algorithm converges faster than the ELMS algorithm and has steady-state errors comparable to that of the LMS algorithm. We analyze the mean-square performance of ELMS and FELMS algorithms theoretically and derive the respective convergence conditions as well as the predicted MSD values. In addition, we present an adaptive sampling strategy in which the sampling probability of each node is changed according to the GMSD of the node. Computer simulations using both synthetic and real data validate the theoretical results and demonstrate the merits of the proposed algorithms.

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Section: B Fast Elms For Graph Signalsmentioning

confidence: 99%

Efficient Estimation of Graph Signals With Adaptive Sampling

Ahmadi

Arablouei

Abdolee

2020

IEEE Trans. Signal Process.

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“…Although the DAPA and DSAF can improve the convergence when the input is colored, the complexity for DAPA is increased. DSAF is more popular because of its simple computational complexity that is similar to the DLMS [11][12][13][14][15]. However, the above mentioned algorithms are all developed based on the l 2 -norm optimization criterion, whose performance will be seriously degraded under non-Gaussian interference.…”

Section: Introductionmentioning

confidence: 99%

Diffusion Correntropy Subband Adaptive Filtering (SAF) Algorithm over Distributed Smart Dust Networks

Guo

2019

Symmetry

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The diffusion subband adaptive filtering (DSAF) algorithm has attracted much attention in recent years due to its decorrelation ability for colored input signals. In this paper, a modified DSAF algorithm using the symmetry maximum correntropy criterion (MCC) with individual weighting factors is proposed and discussed to combat impulsive noise, which is denoted as the MCC-DSAF algorithm. During the iterations, the negative exponent in the Gaussian kernel of the MCC-DSAF eliminates the interference of outliers to provide a robust performance in non-Gaussian noise environments. Moreover, in order to enhance the convergence for sparse system identifications, a variant of MCC-DSAF named as improved proportionate MCC-DSAF (MCC-IPDSAF) is presented and investigated, which provides a dynamic gain assignment matrix in the MCC-DSAF to adjust the weighted values of each coefficient. Simulation results verify that the newly presented MCC-DSAF and MCC-IPDSAF algorithms are superior to the popular DSAF algorithms.

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