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We address the problem of decentralized joint sparsity pattern recovery based on 1-bit compressive measurements in a distributed network. We assume that the distributed nodes observe sparse signals which share the same but unknown sparsity pattern. Each node obtains measurements via random projections and further quantizes its measurement vector element-wise to 1-bit. We develop two decentralized...
We address the problem of joint sparsity pattern recovery based on multiple measurement vectors (MMVs) in resource constrained distributed networks. We assume that distributed nodes observe sparse signals that share a common (but unknown) sparsity pattern. Each node is assumed to sample the sparse signals via different sensing matrices in general. In many distributed communication networks, it is...
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