Blind separation of sparse sources in the presence of outliers

Chenot, Ccile; Bobin, J.

doi:10.1016/j.sigpro.2017.03.024

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…Since the transmit and receive antenna work on the same frequency band, traditional interference cancellation techniques are invalid. As our previous work shown [5], blind source separation (BSS) [6] has an excellent capability to address such issue.…”

Section: Introductionmentioning

confidence: 99%

Blind Source Separation for Complex-Valued Signals Using Generalized Autocorrelation

Tang

Wang

2018

Mathematical Problems in Engineering

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We introduce a new complex-valued blind source separation approach, based on generalized autocorrelations of sources, to improve the spectrum efficiency for the next-generation wireless communications system. The proposed algorithm considers the temporal structures of communication signals and the natural gradient-based method is used to optimize the demixing matrix. In addition, the local stability condition is proved. Simulation results are presented showing the superior performance of the proposed algorithm in the intersymbol interference of the estimated signals.

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Section: Introductionmentioning

confidence: 99%

Blind Source Separation for Complex-Valued Signals Using Generalized Autocorrelation

Tang

Wang

2018

Mathematical Problems in Engineering

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“…Unfortunately, neither the low-rankness nor the non-negativity assumptions are valid in a broad range of applications such as the Planck data. For that purpose, we introduced in [14] a robust BSS algorithm coined rAMCA that jointly estimates A, S and O. The rAMCA algorithm builds upon the sparse modeling of the sources and the outliers in the same dictionary.…”

Section: Lo:x=l+omentioning

confidence: 99%

“…O and the sources S are the solutions of Problem (14). Since, for fixed sources, updating the outliers allows a closed-form expression, we opted for the BCD strategy that alternates between estimations of O and S:…”

Section: Estimating O and S Using The Morphological Diversity For Fimentioning

confidence: 99%

“…This weighting strategy aims at penalizing the samples of X −Õ which behave as corrupted samples, and which can be traced using the sparsity level of the estimated expansion coefficients of the sources [4]. The AMCA algorithm has been used to improve the separation of A and S in the presence of outliers when no morphological diversity can help distinguishing between the sources and the outliers [14].…”

Section: Estimating O and S Using The Morphological Diversity For Fimentioning

confidence: 99%

“…This strategy has been developed essentially with non-negativity [40], [54] and low-rank priors for hyperspectral unmixing [1], [21], [33]. In [14], we proposed a robust BSS method assuming that both the outliers and the sources are sparsely represented in a same domain. The proposed method estimates reliably the mixing matrix, but was however unable to separate precisely the sources and the outliers without additional assumption (see Section…”

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confidence: 99%

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Blind Source Separation with Outliers in Transformed Domains

Chenot¹,

Bobin²

2018

SIAM J. Imaging Sci.

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Blind Source Separation (BSS) methods are well suited for the analysis of multichannel data. In many applications, the observations are corrupted by an additional structured noise, which hinders most of the standard BSS techniques. In this article, we propose a novel BSS method able to jointly unmix the sources and separate the source contribution from the structured noise or outliers. This separation builds upon the difference of morphology between the components of interest, often encountered in imaging problems, by exploiting a sparse modeling of the components in two different domains. Numerical experiments highlight the robustness and precision of the proposed method in a wide variety of settings, including the full-rank regime.

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Simplicity-based recovery of finite-alphabet signals for large-scale MIMO systems

Hajji¹,

Bey²,

Amis³

2018

Digital Signal Processing

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In this paper, we consider the problem of finite-alphabet source separation in both determined and underdetermined large-scale systems. First, we address the noiseless case and we propose a linear criterion based on 1 -minimization combined with box constraints. We investigate also the system conditions that ensure successful recovery. Next, we apply the approach to the noisy massive MIMO transmission and we propose a quadratic criterion-based detector. Simulation results show the efficiency of the proposed detection methods for various QAM modulations and MIMO configurations. We mention that there is no change in the computational complexity when the constellation size increases. Moreover, the proposed method outperforms the classical Minimum Mean Square Error (MMSE)-based detection algorithms.

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Blind separation of sparse sources in the presence of outliers

Cited by 9 publications

References 26 publications

Blind Source Separation for Complex-Valued Signals Using Generalized Autocorrelation

Blind Source Separation for Complex-Valued Signals Using Generalized Autocorrelation

Blind Source Separation with Outliers in Transformed Domains

Simplicity-based recovery of finite-alphabet signals for large-scale MIMO systems

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