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In this paper, we address the problem of sparse signal recovery from scalar quantized compressed sensing measurements, via optimization. To compensate for compression losses due to dimensionality reduction and quantization, we consider a cost function that is more sparsity-inducing than the commonly used ℓ1-norm. Besides, we enforce a quantization consistency constraint that naturally handles the...
In this paper, we address the problem of sparse signal recovery, from multi-bit scalar quantized compressed sensing measurements, where the saturation issue is taken into account. We propose a convex optimization approach, where saturation errors are jointly estimated with the sparse signal to be recovered. In the proposed approach, saturated measurements, even though over-identified, are considered...
We consider the problem of signal recovery under a sparsity prior, from multi-bit quantized compressed measurements. Recently, it has been shown that allowing a small fraction of the quantized measurements to saturate, combined with a saturation consistency recovery approach, would enhance reconstruction performance. In this paper, by leveraging the potential sparsity of the corrupting saturation...
In this paper, we propose a novel approach for multiple antenna spectrum sensing based on compressed sensing. Our focus is on the angular sparsity of the received signal given an unknown number of primary user source signals impinging upon the antenna array from different Directions Of Arrival (DOA). Given multiple snapshots over a small time period, multiple measurement vectors are available and...
Eigenvalue based detection is one of the most promising blind spectrum sensing techniques in cognitive radio. However, it suffers from computational complexity resulting from sample covariance matrix computation and eigenvalue decom-position. In this paper, we propose a reduced-complexity blind detection algorithm to detect the presence of a main direction in terms of energy. Instead of eigenvalue...
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