Cited 0 time in
Cited 0 time in
Sparse Signal Recovery via Tree Search Matching Pursuit
- Sparse Signal Recovery via Tree Search Matching Pursuit
- Lee, J[Lee, Jaeseok]; Choi, JW[Choi, Jun Won]; Shim, B[Shim, Byonghyo]
- DGIST Authors
- Lee, J[Lee, Jaeseok]
- Issue Date
- Journal of Communications and Networks, 18(5), 699-712
- Article Type
- Compressive Sensing; Cost Effectiveness; Greedy Algorithm; Greedy Algorithms; Internet of Things (IOT); Iterative Methods; Recovery; Signal Reconstruction; Sparse Recovery; Tree Pruning; Tree Search; Trees (Mathematics)
- 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.
- Korea Information and Communications Society
There are no files associated with this item.
- Information and Communication EngineeringETC1. Journal Articles
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.