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The problem of constructing matrix with lowcoherence is arised in many applications, such as CDMA, compressive sensing (CS), beamforming, etc. Usually the design of low-coherence codebook can be modeled as vector quantization (VQ) problem, and generalized Lloyd algorithm is designed to solve it. Since Lloyd algorithm is a method of local optimization, its performance is influenced by initial value...
The correlation based framework has recently been proposed for sparse support recovery in noiseless case. To solve this framework, the constrained least absolute shrinkage and selection operator (LASSO) was employed. The regularization parameter in the constrained LASSO was found to be a key to the recovery. This paper will discuss the sparse support recoverability via the framework and adjustment...
In this paper, a block version of the orthogonal matching pursuit with thresholding algorithm is proposed. Compared with the block version of the orthogonal matching pursuit algorithm, the block orthogonal matching pursuit algorithm works in a less greedy fashion in order to improve support estimating efficiency in each iteration. The lower and upper bounds of the threshold are theoretical derived...
In this paper, the Positive constrained Least Absolute Shrinkage and Selection Operator (P-LASSO) is studied for sparse support recovery using the correlation information in Compressive sensing (CS). A structural constraint is obtained for selecting the regularization parameter in the case of additive Gaussian noise. Since the measurements are finite in practice, the probability of successful recovering...
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