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UCS-NT: An unbiased compressive sensing framework for Network Tomography

Mahyar, H ; Sharif University of Technology | 2013

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  1. Type of Document: Article
  2. DOI: 10.1109/ICASSP.2013.6638518
  3. Publisher: 2013
  4. Abstract:
  5. This paper 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 for sparse recovery in networks. In order to efficiently recover sparse specification of link vectors, we construct a feasible measurement matrix using this framework through connected paths. It is theoretically shown that, only O(k log(n)) path measurements are sufficient for uniquely recovering any k-sparse link vector. Moreover, extensive simulations demonstrate that this framework would converge to an accurate solution for a wide class of networks
  6. Keywords:
  7. Compressive sensing ; Extensive simulations ; Measurement matrix ; Network Monitoring ; Network tomography ; Sparse recovery ; Tomography applications ; Topological constraints ; Computer simulation ; Recovery ; Signal reconstruction ; Computer system recovery
  8. Source: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings ; 2013 , Pages 4534-4538 ; 15206149 (ISSN) ; 9781479903566 (ISBN)
  9. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6638518