Towards Unbiased Compressive Sensing for Network Tomography

Author: Hamidreza Mahyar and Hamid R. Rabiee
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.
Sat, 11/10/2012