Narrowband interference suppression in impulsive channels

Garth, L.M.; Poor, H. Vincent

doi:10.1109/7.135430

Cited by 48 publications

(21 citation statements)

References 20 publications

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“…noise, KF-based algorithms can diverge or fail to provide reliable and consistent state estimates. Different non-Gaussian noise models have been considered thus far [25], [26], among which the focus of the letter is on Gaussian-mixture (GM) models [27] which relate to many physical non-Gaussian phenomena. The GM model is rich enough to approximate several non-Gaussian density functions of practical engineering interest [6].…”

Section: Introductionmentioning

confidence: 99%

Complex-Valued Gaussian Sum Filter for Nonlinear Filtering of Non-Gaussian/Non-Circular Noise

Mohammadi

Plataniotis

2015

IEEE Signal Process. Lett.

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Motivated by application of Gaussian sum filters (GSF) and multiple model adaptive estimation (MMAE) approaches in scenarios where assumption of proper (circular) Gaussian signals is not valid, the letter proposes a novel complexvalued Gaussian sum filter (C/GSF) for non-linear filtering of non-Gaussian/non-circular measurement noise. Although the literature on recursive state estimation using GSF is rich, its complex-valued counterpart which incorporates the full second-order statistics of the system and can cope with nonGaussian/non-circular measurements, has not yet been investigated in the literature. The paper addresses this gap. The C/GSF is a computationally attractive adaptive filter where the number of non-circular Gaussian components is controlled utilizing a modified Bayesian learning technique which is used to collapse the resulting non-Gaussian sum mixture into an equivalent complex-valued Gaussian term. Simulation results indicate that the C/GSF provides significant performance improvement over its traditional counterparts.

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Section: Introductionmentioning

confidence: 99%

Complex-Valued Gaussian Sum Filter for Nonlinear Filtering of Non-Gaussian/Non-Circular Noise

Mohammadi

Plataniotis

2015

IEEE Signal Process. Lett.

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“…For simplicity, we drop the symbol index and denote . Then, (6) can be rewritten as (9) or in matrix notation (10) where , and . Consider the linear regression problem of estimating the unknown parameters from the observations in (9).…”

Section: A Least Squares Regression and The Linear Decorrelatormentioning

confidence: 99%

“…Then, (6) can be rewritten as (9) or in matrix notation (10) where , and . Consider the linear regression problem of estimating the unknown parameters from the observations in (9). Classically, this problem can be solved by minimizing the sum of squared errors, i.e., through the least-squares (LS) method (11) …”

Section: A Least Squares Regression and The Linear Decorrelatormentioning

confidence: 99%

“…However, since non-Gaussian noise can, in fact, be beneficial to system performance if properly treated, the problem of joint mitigation of structured interference and non-Gaussian ambient noise is of interest [22]. An approach to this problem for narrowband interference (NBI) suppression in spread-spectrum systems is described in [9]. A recent study [23] has shown that the performance gains afforded by maximum likelihood (ML) multiuser detection in impulsive noise can be substantial when compared with optimum multiuser detection based on a Gaussian noise assumption.…”

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confidence: 99%

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Improved robust multiuser detection in non-Gaussian channels

Spasojević

Wang

2001

IEEE Signal Process. Lett.

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Abstract-In many wireless systems where multiuser detection techniques may be applied, the ambient channel noise is known through experimental measurements to be decidedly nonGaussian, due largely to impulsive phenomena. The performance of many multiuser detectors can degrade substantially in the presence of such impulsive ambient noise. In this paper, we develop robust multiuser detection techniques for combating multipleaccess interference and impulsive noise in CDMA communication systems. These techniques are based on the M-estimation method for robust regression. Analytical and simulation results show that the proposed robust techniques offer significant performance gain over linear multiuser detectors in impulsive noise, with little attendant increase in computational complexity.We also develop a subspace-based technique for blind adaptive implementation of the robust multiuser detectors, which requires only the signature waveform and the timing of the desired user in order to demodulate that user's signal. The robust multiuser detection technique and its blind adaptive version can be applied to both synchronous and asynchronous CDMA channels.

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“…However, Since non-Gaussian noise can, in fact, be beneficial to system performance if properly treated [11], the problem of joint mitigation of structured interference and non-Gaussian ambient noise has received considerable recent interests. An approach to this problem for NBI suppression in spread-spectrum systems is described in [8]. Some very recent results that describe nonlinear adaptive methods for suppressing MAI in the presence of impulsive noise in CDMA communication systems are found in [26,29].…”

Section: Impulsive Ambient Noise Rejectionmentioning

confidence: 99%