Towards Unbiased Compressive Sensing for Network Tomography

Author: Hamidreza Mahyar and Hamid R. Rabiee
Abstract: 
This document addresses the problem of recovering sparse link vectors with network topological constraints that is motivated by network inference and tomography applications. We propose a novel framework called UCS-NT in the context of compressive sensing to efficiently recover sparse vectors representing the properties of the links from complex networks. To design a fea- sible measurement matrix, we construct a sufficient number of collective additive measurements using this framework through connected paths. It is theoretically shown that, only O(k log(n)) path measurements are enough for uniquely recovering any k-sparse link vector with no more than k nonzero elements. Moreover, extensive simulations demonstrate that this framework would converge to an accurate solution.
Date: 
Sat, 11/10/2012

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