Adaptive Sparse Channel Estimation under Symmetric alpha-Stable Noise

Pelekanakis, Konstantinos; Chitre, Mandar

doi:10.1109/twc.2014.042314.131432

Cited by 103 publications

(52 citation statements)

References 36 publications

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“…Here, e i;D ðkÞ is assumed to be Gaussian distributed except when being corrupted occasionally by additive impulsive noise [21,26], and the following robust variance estimate is adopted:…”

Section: Parameters Estimation and Computational Complexitymentioning

confidence: 99%

Affine projection M-estimate subband adaptive filters for robust adaptive filtering in impulsive noise

Zheng

Zhao

2016

Signal Processing

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Section: Parameters Estimation and Computational Complexitymentioning

confidence: 99%

Affine projection M-estimate subband adaptive filters for robust adaptive filtering in impulsive noise

Zheng

Zhao

2016

Signal Processing

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“…In such situations, the MMSE may lead to poor performance, especially when the noise has impulsive characteristics. To overcome this issue, some robust algorithms are taken into consideration by researchers [15][16][17]. Sayin et al [15] proposed a novel family of adaptive filtering algorithms based on the logarithmic cost, which are robust to the impulsive noises.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the logarithmic cost function is also used to develop an entropyfunctional-based online adaptive decision fusion (EADF) framework [16]. Two novel frameworks (RLS-type and NGtype algorithms) are proposed in [17] for designing online adaptive algorithms that exploit channel sparseness. However, the studies of sparse channel estimation in the presence of impulsive noise are still scarce.…”

Section: Introductionmentioning

confidence: 99%

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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“…Most of the current single-carrier coherent UWA communication receivers are evolved from the receiver proposed by Stojanovic and Proakis. Generally speaking, a lot of work has been done to improve the performance of the UWA coherent receivers, such as improving the adaptive DFE algorithm [4][5][6], adding pre-processing technique [7,8], applying array processing technique [9][10][11][12], and channel coding technique [13][14][15][16]. Even if a lot of researchers, such as Freitag and Stojanovie [17] and Blackmon and Canto [18] have done a lot of work to improve the performance of the adaptive DFE algorithms in the receiver, the computing complexity is the main obstacle for hardware implementation.…”

Section: Introductionmentioning

confidence: 99%

Single‐carrier underwater acoustic communication combined with channel shortening and dichotomous coordinate descent recursive least squares with variable forgetting factor

Zhang

Sun

et al. 2015

IET Communications

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In this study, the authors propose a novel decision feedback equaliser (DFE)-based receiver, which combines channel shortening methods and dichotomous coordinate descent (DCD) recursive least squares (RLS) adaptive algorithm with variable forgetting factor (VFF). The proposed receiver can obtain its performance gain via two aspects: (i) reducing the computing complexity by channel shortening method, such as passive time reversal (pTR) filter or minimised mean-square error (MMSE) filter and (ii) improving the performance of the receiver, such as a lower bit error rate and mean-square error. Underwater acoustic (UWA) channel has the features of long multi-path spread and time-varying property. When the multi-path spread is very long, pTR or MMSE filter is used as a channel shortening pre-processing method to reduce the computing complexity in the DFE receiver. When the channel is time varying, VFF is incorporated into DCD-RLS adaptive algorithm to improve the tracking capability of the DFE receiver. The proposed receiver is appealing for practical implementations. Numerical examples and lake experimental results show that, the proposed DFE receiver can achieve a better performance in the UWA communication system.

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Adaptive Sparse Channel Estimation under Symmetric alpha-Stable Noise

Cited by 103 publications

References 36 publications

Affine projection M-estimate subband adaptive filters for robust adaptive filtering in impulsive noise

Affine projection M-estimate subband adaptive filters for robust adaptive filtering in impulsive noise

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

Single‐carrier underwater acoustic communication combined with channel shortening and dichotomous coordinate descent recursive least squares with variable forgetting factor

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