Decentralized and Collaborative Subspace Pursuit: A Communication-Efficient Algorithm for Joint Sparsity Pattern Recovery With Sensor Networks

In this paper, we consider the problem of joint sparsity pattern recovery in a distributed sensor network. The sparse multiple measurement vector signals (MMVs) observed by all the nodes are assumed to have a common (but unknown) sparsity pattern. To accurately recover the common sparsity pattern in...

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Published in:	IEEE transactions on signal processing Vol. 64; no. 3; pp. 556 - 566
Main Authors:	Gang Li, Wimalajeewa, Thakshila, Varshney, Pramod K.
Format:	Journal Article
Language:	English
Published:	IEEE 01.02.2016
Subjects:	Algorithm design and analysis Algorithms Collaboration compressive sensing Decentralized Estimates information fusion Joint sparsity pattern recovery Matching pursuit algorithms Mathematical analysis Networks Recovery Signal processing algorithms Sparse representation subspace pursuit Subspaces Vectors (mathematics)
ISSN:	1053-587X, 1941-0476
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Abstract	In this paper, we consider the problem of joint sparsity pattern recovery in a distributed sensor network. The sparse multiple measurement vector signals (MMVs) observed by all the nodes are assumed to have a common (but unknown) sparsity pattern. To accurately recover the common sparsity pattern in a decentralized manner with a low communication overhead of the network, we develop an algorithm named decentralized and collaborative subspace pursuit (DCSP). In DCSP, each node is required to perform three kinds of operations per iteration: 1) estimate the local sparsity pattern by finding the subspace that its measurement vector most probably lies in; 2) share its local sparsity pattern estimate with one-hop neighboring nodes; and 3) update the final sparsity pattern estimate by majority vote based fusion of all the local sparsity pattern estimates obtained in its neighborhood. The convergence of DCSP is proved and its communication overhead is quantitatively analyzed. We also propose another decentralized algorithm named generalized DCSP (GDCSP) by allowing more information exchange among neighboring nodes to further improve the accuracy of sparsity pattern recovery at the cost of increased communication overhead. Experimental results show that, 1) compared with existing decentralized algorithms, DCSP provides much better accuracy of sparsity pattern recovery at a comparable communication cost; and 2) the accuracy of GDCSP is very close to that of centralized processing.
AbstractList	In this paper, we consider the problem of joint sparsity pattern recovery in a distributed sensor network. The sparse multiple measurement vector signals (MMVs) observed by all the nodes are assumed to have a common (but unknown) sparsity pattern. To accurately recover the common sparsity pattern in a decentralized manner with a low communication overhead of the network, we develop an algorithm named decentralized and collaborative subspace pursuit (DCSP). In DCSP, each node is required to perform three kinds of operations per iteration: 1) estimate the local sparsity pattern by finding the subspace that its measurement vector most probably lies in; 2) share its local sparsity pattern estimate with one-hop neighboring nodes; and 3) update the final sparsity pattern estimate by majority vote based fusion of all the local sparsity pattern estimates obtained in its neighborhood. The convergence of DCSP is proved and its communication overhead is quantitatively analyzed. We also propose another decentralized algorithm named generalized DCSP (GDCSP) by allowing more information exchange among neighboring nodes to further improve the accuracy of sparsity pattern recovery at the cost of increased communication overhead. Experimental results show that, 1) compared with existing decentralized algorithms, DCSP provides much better accuracy of sparsity pattern recovery at a comparable communication cost; and 2) the accuracy of GDCSP is very close to that of centralized processing.
Author	Varshney, Pramod K. Gang Li Wimalajeewa, Thakshila
Author_xml	– sequence: 1 surname: Gang Li fullname: Gang Li email: gangli@tsinghua.edu.cn organization: Dept. of Electron. Eng., Tsinghua Univ., Beijing, China – sequence: 2 givenname: Thakshila surname: Wimalajeewa fullname: Wimalajeewa, Thakshila email: twwewelw@syr.edu organization: Dept. of Electr. Eng. & Comput. Sci., Syracuse Univ., Syracuse, NY, USA – sequence: 3 givenname: Pramod K. surname: Varshney fullname: Varshney, Pramod K. email: varshney@syr.edu organization: Dept. of Electr. Eng. & Comput. Sci., Syracuse Univ., Syracuse, NY, USA
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SubjectTerms	Algorithm design and analysis Algorithms Collaboration compressive sensing Decentralized Estimates information fusion Joint sparsity pattern recovery Matching pursuit algorithms Mathematical analysis Networks Recovery Signal processing algorithms Sparse representation subspace pursuit Subspaces Vectors (mathematics)
Title	Decentralized and Collaborative Subspace Pursuit: A Communication-Efficient Algorithm for Joint Sparsity Pattern Recovery With Sensor Networks
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