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- W1779291257 abstract "In order to improve energy efficiency in the process of gathering data and transmitting information, compressed sensing (CS) has been applied increasingly in wireless local area networks (WLANs). In traditional usages of CS techniques in previous literatures, the sparsities of the signals have to be known beforehand, which is significant for the recovered results. However, it is difficult to realize precisely the structures of the actual signals in WLANs. Therefore, it is important to further exploit the reasonable practicality of signals in actual applications. In this paper, we present a spatial–temporal correlation model to optimize measure matrix of CS on the basis of a local region. Furthermore, two algorithms based on two kinds of situations are designed, both of which are pervasive data distributions in real environments. Our algorithms have been proven to be valuable and could be considered for actual applications. Experiments show that the signals could be reconstructed accurately and stably even if their sparsities could not be known in advance. Copyright © 2013 John Wiley & Sons, Ltd." @default.
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- W1779291257 date "2013-01-01" @default.
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- W1779291257 title "Distributed compressed sensing in wireless local area networks" @default.
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- W1779291257 doi "https://doi.org/10.1002/dac.2501" @default.
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