TY - GEN
T1 - Distributed compressed sensing in dynamic networks
AU - Patterson, Stacy
AU - Eldar, Yonina C.
AU - Keidar, Idit
N1 - This work is funded in part by: a Technion fellowship, the Lady Davis Fellowship Trust, the Israel Science Foundation under grant no. 1549/_11, the Intel Collaborative Research Institute for Computational Intelligence (ICRI-CI), and the Technion Autonomous Systems Program.
PY - 2013
Y1 - 2013
N2 - We consider the problem of in-network compressed sensing, where the goal is to recover a global, sparse signal from local measurements using only local computation and communication. Our approach to this distributed compressed sensing problem is based on the centralized Iterative Hard Thresholding algorithm (IHT). In time-varying networks, the network dynamics necessarily introduce inaccuracies that are not present in a centralized implementation of IHT. To accommodate these inaccuracies, we show how centralized IHT can be extended to include inexact computations while still providing the same recovery guarantees. We then leverage these new theoretical results to develop a distributed version of IHT for dynamic networks. Evaluations show that our algorithm outperforms the best-known existing solution in both time and bandwidth by several orders of magnitude.
AB - We consider the problem of in-network compressed sensing, where the goal is to recover a global, sparse signal from local measurements using only local computation and communication. Our approach to this distributed compressed sensing problem is based on the centralized Iterative Hard Thresholding algorithm (IHT). In time-varying networks, the network dynamics necessarily introduce inaccuracies that are not present in a centralized implementation of IHT. To accommodate these inaccuracies, we show how centralized IHT can be extended to include inexact computations while still providing the same recovery guarantees. We then leverage these new theoretical results to develop a distributed version of IHT for dynamic networks. Evaluations show that our algorithm outperforms the best-known existing solution in both time and bandwidth by several orders of magnitude.
KW - Distributed algorithm
KW - Distributed consensus
KW - Iterative hard thresholding
UR - http://www.scopus.com/inward/record.url?scp=84897713168&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP.2013.6737038
DO - 10.1109/GlobalSIP.2013.6737038
M3 - منشور من مؤتمر
SN - 9781479902484
T3 - 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
SP - 903
EP - 906
BT - 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
T2 - 2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013
Y2 - 3 December 2013 through 5 December 2013
ER -