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http://dx.doi.org/10.1109/JCN.2016.000100

Sparse Signal Recovery via Tree Search Matching Pursuit  

Lee, Jaeseok (Dept. of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology)
Choi, Jun Won (Dept. of Electrical Engineering, Hanyang University)
Shim, Byonghyo (Institute of New Media and Communications and School of Electrical and Computer Engineering, Seoul National University)
Publication Information
Journal of Communications and Networks / v.18, no.5, 2016 , pp. 699-712 More about this Journal
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.
Keywords
Compressive sensing; greedy algorithm; sparse recovery; tree pruning; tree search;
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