Niels Lovmand Pedersen

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Sparse modeling and estimation of complex signals is not uncommon in practice. However, historically, much attention has been drawn to real-valued system models, lacking the research of sparse signal modeling and estimation for complex-valued models. This paper introduces a unifying sparse Bayesian formalism that generalizes to complexas well as real-valued(More)
Existing methods for sparse channel estimation typically provide an estimate computed as the solution maximizing an objective function defined as the sum of the log-likelihood function and a penalization term proportional to the l1-norm of the parameter of interest. However, other penalization terms have proven to have strong sparsity-inducing properties.(More)
In sparse Bayesian learning (SBL), Gaussian scale mixtures (GSMs) have been used to model sparsity-inducing priors that realize a class of concave penalty functions for the regression task in real-valued signal models. Motivated by the relative scarcity of formal tools for SBL in complex-valued models, this paper proposes a GSM model the Bessel K model that(More)
In this paper, we present a Bayesian channel estimation algorithm for multicarrier receivers based on pilot symbol observations. The inherent sparse nature of wireless multipath channels is exploited by modeling the prior distribution of multipath components’ gains with a hierarchical representation of the Bessel K probability density function; a highly(More)
We derive low complexity versions of a wide range of algorithms for sparse Bayesian learning (SBL) in underdetermined linear systems. The proposed algorithms are obtained by applying the generalized mean field (GMF) inference framework to a generic SBL probabilistic model. In the GMF framework, we constrain the auxiliary function approximating the posterior(More)
In sparse Bayesian learning (SBL) approximate Bayesian inference is applied to find sparse estimates from observations corrupted by additive noise. Current literature only vaguely considers the case where the noise level is unknown a priori. We show that for most state-of-the-art reconstruction algorithms based on the fast inference scheme noise precision(More)
Traditionally, the dictionary matrices used in sparse wireless channel estimation have been based on the discrete Fourier transform, following the assumption that the channel frequency response (CFR) can be approximated as a linear combination of a small number of multipath components, each one being contributed by a specific propagation path. In practical(More)
1. Abstract Composite plates are designed in order to maximize the performance with respect to eigenfrequencies. The plates are considered to be laminates where the individual plies consist of orthotropic material. The design task is the orientation of the orthotropic material in each element of the discretization and the ratio between the amounts of(More)
In this paper, we present an investigation on the impact of spatial smoothing and forward-backward averaging techniques for subspace-based channel estimation. The spatial smoothing technique requires the selection of a window size, which, if not chosen properly, leads to dramatic performance breakdown of subspace-based methods. We provide an explanation of(More)
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