Estimation of Multipath Channels With Long Impulse Response at Low SNR via an MCMC Method

Rabaste, Olivier; Chonavel, Thierry

doi:10.1109/tsp.2006.888060

Cited by 40 publications

(30 citation statements)

References 36 publications

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“…minimizes the probability of a component error P{b k = b k }; it is also known as "maximum posterior marginal/mode (MPM) detector" (e.g., [45]). It can be shown thatb…”

Section: A Sequence and Component Detectorsmentioning

confidence: 99%

Blind Deconvolution of Sparse Pulse Sequences Under a Minimum Distance Constraint: A Partially Collapsed Gibbs Sampler Method

Kail¹,

Tourneret

Hlawatsch³

et al. 2012

IEEE Trans. Signal Process.

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Abstract-For blind deconvolution of an unknown sparse sequence convolved with an unknown pulse, a powerful Bayesian method employs the Gibbs sampler in combination with a Bernoulli-Gaussian prior modeling sparsity. In this paper, we extend this method by introducing a minimum distance constraint for the pulses in the sequence. This is physically relevant in applications including layer detection, medical imaging, seismology, and multipath parameter estimation. We propose a Bayesian method for blind deconvolution that is based on a modified Bernoulli-Gaussian prior including a minimum distance constraint factor. The core of our method is a partially collapsed Gibbs sampler (PCGS) that tolerates and even exploits the strong local dependencies introduced by the minimum distance constraint. Simulation results demonstrate significant performance gains compared to a recently proposed PCGS. The main advantages of the minimum distance constraint are a substantial reduction of computational complexity and of the number of spurious components in the deconvolution result.

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“…minimizes the probability of a component error P{b k = b k }; it is also known as "maximum posterior marginal/mode (MPM) detector" (e.g., [45]). It can be shown thatb…”

Section: A Sequence and Component Detectorsmentioning

confidence: 99%

Blind Deconvolution of Sparse Pulse Sequences Under a Minimum Distance Constraint: A Partially Collapsed Gibbs Sampler Method

Kail¹,

Tourneret

Hlawatsch³

et al. 2012

IEEE Trans. Signal Process.

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“…Note thatĤ s jj is the estimate of the channel matrix H jj based on the proposed subspacebased SBCE approach. Upon comparing (9) to (17), it can be seen that the ambiguity matrixẼ jj is completely resolved in the channel estimateĤ s jj , and the residual estimation error is caused by the ICI, the intra-cell interference and the noise. It can be seen from (17) that…”

Section: B the Proposed Sbcementioning

confidence: 99%

Semi-Blind Channel Estimation Relying on Optimum Pilots Designed for Multi-Cell Large-Scale MIMO Systems

Yang

Gao

2016

IEEE Access

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Channel estimation in the presence of pilot contamination-induced inter-cell interference (ICI) is a major challenge in large-scale multiple-input multiple-output (LS-MIMO) systems. In this paper, a subspace-based semi-blind channel estimator (SBCE) relying on an optimum pilot design (in the sense of minimizing the channel estimation error covariance) is proposed for LS-MIMO systems. The proposed SBCE is capable of exploiting the asymptotic orthogonality of the channel vectors encountered in LS-MIMO systems, while taking advantage of both the optimized pilots and the data symbols. In order to ensure the best-possible performance of the proposed SBCE, we analyze the properties to be satisfied by the optimal pilots and then design these pilots relying on Zadoff-Chu sequences. As a beneficial result, the intra-cell interference is completely eliminated, and the ICI is substantially reduced. Our analytical and numerical results confirm that the performance of the proposed SBCE is superior to that of the representative stateof-the-art channel estimators in practical LS-MIMO systems, which have a finite number of base station antennas and data symbols available to be capitalized on for channel estimation.INDEX TERMS Large-scale/massive multiple-input multiple-output (LS-MIMO/massive MIMO), semi-blind, channel estimation, pilot design, inter-cell interference (ICI).

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“…wherex represents the conjugate of x. for a PRN sequence, the ambiguity function is approximately [10]:…”

Section: Ambiguity Functionmentioning

confidence: 99%

Near sea surface target tracking by Extended Kalman filtering of the GPS reflected signals

Tay

Maussang

Coatanhay

et al. 2011

OCEANS 2011 IEEE - Spain

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This paper addresses the use of Global Navigation Satellite Systems (GNSS) as a remote sensing tool for oceanographic applications. In this paper we use the Global Positioning System (GPS) signals reflected off the sea surface along with a coastal receiver to perform detection and tracking of a near sea surface mobile target. Because these signals have a very low Signal to Noise Ratio (SNR) in a non stationary medium, a matched filter is required. A filter based on the Extended Kalman process is presented here.

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Estimation of Multipath Channels With Long Impulse Response at Low SNR via an MCMC Method

Cited by 40 publications

References 36 publications

Blind Deconvolution of Sparse Pulse Sequences Under a Minimum Distance Constraint: A Partially Collapsed Gibbs Sampler Method

Blind Deconvolution of Sparse Pulse Sequences Under a Minimum Distance Constraint: A Partially Collapsed Gibbs Sampler Method

Semi-Blind Channel Estimation Relying on Optimum Pilots Designed for Multi-Cell Large-Scale MIMO Systems

Near sea surface target tracking by Extended Kalman filtering of the GPS reflected signals

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