Network Tomography: Identifiability and Fourier Domain Estimation

Details Network Tomography: Identifiability and Fourier Domain Estimation Network Tomography: Identifiability and Fourier Domain Estimation

2007-12-21

arxiv.org/abs/0712.3618v1

IEEE INFOCOM 2007, p.1875-1883

Statistics

Methodology

21 pages

Scientific paper

10.1109/INFCOM.2007.218

The statistical problem for network tomography is to infer the distribution of $\mathbf{X}$, with mutually independent components, from a measurement model $\mathbf{Y}=A\mathbf{X}$, where $A$ is a given binary matrix representing the routing topology of a network under consideration. The challenge is that the dimension of $\mathbf{X}$ is much larger than that of $\mathbf{Y}$ and thus the problem is often called ill-posed. This paper studies some statistical aspects of network tomography. We first address the identifiability issue and prove that the $\mathbf{X}$ distribution is identifiable up to a shift parameter under mild conditions. We then use a mixture model of characteristic functions to derive a fast algorithm for estimating the distribution of $\mathbf{X}$ based on the General method of Moments. Through extensive model simulation and real Internet trace driven simulation, the proposed approach is shown to be favorable comparing to previous methods using simple discretization for inferring link delays in a heterogeneous network.

Affiliated with

Also associated with

No associations

Rating

Network Tomography: Identifiability and Fourier Domain Estimation does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Network Tomography: Identifiability and Fourier Domain Estimation, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Network Tomography: Identifiability and Fourier Domain Estimation will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-695900