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There is a recent interest in developing algorithms for the reconstruction of jointly sparse signals, which arises in a large number of applications. In this work, we study the problem of wide-band spectrum sensing for cognitive radio networks using compressed sensing to exploit the underlying joint sparsity structure in a distributed setting. In particular, we use the recently proposed Approximate...
There is a recent interest in developing algorithms for the reconstruction of jointly sparse signals, which arises in a large number of applications such as sensor networks. In many of these applications, we encounter extremely large problem sizes for which algorithms with low computational complexity are required. Recently, an algorithm called iterative hard thresholding has been proposed, which...
In this paper, we define a new Joint Sparsity Model (JSM) and use Principal Component Analysis followed by Minimum Description Length and Compressive Sensing to reconstruct spatially and temporally correlated signals in a sensor network. The proposed model decomposes each sparse signal into two sparse components. The first component has a common support across all sensed signals. The second component...
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