000169539 001__ 169539
000169539 005__ 20180317093709.0
000169539 020__ $$a978-1-4244-4312-3
000169539 02470 $$2ISI$$a000280141400033
000169539 037__ $$aCONF
000169539 245__ $$aLinear Compressive Networks
000169539 260__ $$bIeee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa$$c2009
000169539 269__ $$a2009
000169539 336__ $$aConference Papers
000169539 520__ $$aA linear compressive network (LCN) is defined as a graph of sensors in which each encoding sensor compresses incoming jointly Gaussian random signals and transmits (potentially) low-dimensional linear projections to neighbors over a noisy uncoded channel. Each sensor has a maximum power to allocate over signal subspaces. The networks of focus are acyclic, directed graphs with multiple sources and multiple destinations. LCN pathways lead to decoding leaf nodes that estimate linear functions of the original high dimensional sources by minimizing a mean squared error (MSE) distortion cost function. An iterative Optimization of local compressive matrices for all graph nodes is developed using an optimal quadratically constrained quadratic program (QCQP) step. The performance of the optimization is marked by power-compression-distortion spectra, with converse bounds based on cut-set arguments. Examples include single layer and multi-layer (e.g. p-layer tree cascades, butterfly) networks. The LCN is a generalization of the Karhunen-Loeve Transform to noisy multi-layer networks, and extends previous approaches for point-to-point and distributed compression-estimation of Gaussian signals. The framework relates to network coding in the noiseless case, and uncoded transmission in the noisy case.
000169539 6531_ $$aDistributed Estimation
000169539 700__ $$aGoela, Naveen
000169539 700__ $$0241387$$aGastpar, Michael$$g122796
000169539 7112_ $$aIEEE International Symposium on Information Theory (ISIT 2009)$$cSeoul, SOUTH KOREA$$dJun 28-Jul 03, 2009
000169539 773__ $$q159-163$$t2009 Ieee International Symposium On Information Theory, Vols 1- 4
000169539 909CO $$ooai:infoscience.tind.io:169539$$pIC$$pconf
000169539 909C0 $$0252408$$pLINX$$xU12434
000169539 937__ $$aEPFL-CONF-169539
000169539 973__ $$aOTHER$$rREVIEWED$$sPUBLISHED
000169539 980__ $$aCONF