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Sparse Signal Recovery via Tree Search Matching Pursuit

Title
Sparse Signal Recovery via Tree Search Matching Pursuit
Authors
Lee, J[Lee, Jaeseok]Choi, JW[Choi, Jun Won]Shim, B[Shim, Byonghyo]
DGIST Authors
Lee, J[Lee, Jaeseok]
Issue Date
2016-10
Citation
Journal of Communications and Networks, 18(5), 699-712
Type
Article
Article Type
Article
Keywords
Compressive SensingCost EffectivenessGreedy AlgorithmGreedy AlgorithmsInternet of Things (IOT)Iterative MethodsRecoverySignal ReconstructionSparse RecoveryTree PruningTree SearchTrees (Mathematics)
ISSN
1229-2370
Abstract
Recently, greedy algorithm has received much attention as a cost-effective means to reconstruct the sparse signals from compressed measurements. Much of previous work has focused on the investigation of a single candidate to identify the support (index set of nonzero elements) of the sparse signals. Well-known drawback of the greedy approach is that the chosen candidate is often not the optimal solution due to the myopic decision in each iteration. In this paper, we propose a tree search based sparse signal recovery algorithm referred to as the tree search matching pursuit (TSMP). Two key ingredients of the proposed TSMP algorithm to control the computational complexity are the pre-selection to put a restriction on columns of the sensing matrix to be investigated and the tree pruning to eliminate unpromising paths from the search tree. In numerical simulations of Internet of Things (IoT) environments, it is shown that TSMP outperforms conventional schemes by a large margin. © 2011 KICS.
URI
http://hdl.handle.net/20.500.11750/2184
DOI
10.1109/JCN.2016.000100
Publisher
Korea Information and Communications Society
Files:
There are no files associated with this item.
Collection:
Information and Communication EngineeringETC1. Journal Articles


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