Blind PARAFAC receivers for DS-CDMA systems

Sidiropoulos, Nicholas D.; Giannakis, Georgios B.; Bro, Rasmus

doi:10.1109/78.824675

Cited by 494 publications

(508 citation statements)

References 43 publications

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“…PARAFAC for complex-valed tensor was proposed and investigated in some real applications such as for DS-CDMA signals [20][21][22] and for MIMO (Multi-Input Multi-Output) dynamic systems [23]. This section extends the proposed algorithms for complex data tensor.…”

Section: Grid Parafac For Complex-valued Tensorsmentioning

confidence: 99%

“…By replacing Y (1) and Y (2) in (20) by their approximations in (15), and setting (20) to zero, we obtain an up-…”

Section: Approach 1: By Directly Minimizing a Cost Functionmentioning

confidence: 99%

“…For 3-D complex tensor, this learning rule simplifies to the COMplex parallel FACtor analysis (COMFAC) approach [20]. The ALS algorithm for the grid PARAFAC to update a sub-factor A (n) (k n ) is given by…”

Section: Grid Parafac For Complex-valued Tensorsmentioning

confidence: 99%

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PARAFAC algorithms for large-scale problems

Phan

Cichocki

2011

Neurocomputing

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Section: Grid Parafac For Complex-valued Tensorsmentioning

confidence: 99%

“…By replacing Y (1) and Y (2) in (20) by their approximations in (15), and setting (20) to zero, we obtain an up-…”

Section: Approach 1: By Directly Minimizing a Cost Functionmentioning

confidence: 99%

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PARAFAC algorithms for large-scale problems

Phan

Cichocki

2011

Neurocomputing

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“…An important advantage of the CP decomposition in comparison to matrix decompositions, such as principal component analysis (PCA), is that it is essentially unique [28,29] up to scale and permutation indeterminacies under mild conditions on the tensor rank, without imposing additional constraints such as orthogonality or independence. In [28,29], the following sufficient condition for essential uniqueness has been derived:…”

Section: Tensor Construction and Cp Modelmentioning

confidence: 99%

Blind source separation of underdetermined mixtures of event-related sources

Niknazar¹,

Becker²,

Rivet³

et al. 2014

Signal Processing

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This paper addresses the problem of blind source separation for underdetermined mixtures (i.e., more sources than sensors) of event-related sources that include quasi-periodic sources (e.g., electrocardiogram (ECG)), sources with synchronized trials (e.g., event-related potentials (ERP)), and amplitude-variant sources. The proposed method is based on two steps: (i) tensor decomposition for underdetermined source separation and (ii) signal extraction by Kalman filtering to recover the source dynamics. A tensor is constructed for each source by synchronizing on the "event" period of the corresponding signal and stacking different periods along the second dimension of the tensor. To cope with the interference from other sources that impede on the extraction of weak signals, two robust tensor decomposition methods are proposed and compared. Then, the state parameters used within a nonlinear dynamic model for the extraction of event-related sources from noisy mixtures are estimated from the loading matrices provided by the first step. The influence of different parameters on the robustness to outliers of the proposed method is examined by numerical simulations. Applied to clinical electroencephalogram (EEG), ECG and magnetocardiogram (MCG), the proposed method exhibits a significantly higher performance in terms of expected signal shape than classical source separation methods such as πCA and FastICA.

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“…Trilinear/multilinear decomposition is thus naturally related to linear algebra for multi-way arrays [18]. Trilinear/multilinear decomposition is used widely in blind receiver detection for Directsequence code-division multiple access (CDMA) system [19], array signal processing [20][21][22][23][24][25][26], blind estimation of Multi-Input-MultiOutput (MIMO) system [27], blind Speech Separation [28], downlink receiver for space-time block-boded CDMA System [29] and multiuser detection for Single-Input-Multi-Output (SIMO) CDMA System [30].…”

Section: Introductionmentioning

confidence: 99%

Quadrilinear Decomposition-Based Blind Signal Detection for Polarization Sensitive Uniform Square Array

Shi¹,

Zhang²

2008

PIER

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Abstract-This paper links the polarization-sensitive-array signal detection problem to quadrilinear decomposition model. Exploiting this link, it generates a deterministic blind quadrilinear decompositionbased signal detection algorithm, which doesn't require DOA (direction of arrival) and polarization information and has blind and robust characteristics. The proposed algorithm fully utilizes the polarization, spatial and temporal diversity. The simulation results reveal that the performance of blind quadrilinear decomposition-based signal detection algorithm for polarization sensitive uniform square array is close to nonblind MMSE method, and even works better than trilinear decomposition algorithm.

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Blind PARAFAC receivers for DS-CDMA systems

Cited by 494 publications

References 43 publications

PARAFAC algorithms for large-scale problems

PARAFAC algorithms for large-scale problems

Blind source separation of underdetermined mixtures of event-related sources

Quadrilinear Decomposition-Based Blind Signal Detection for Polarization Sensitive Uniform Square Array

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