Maximum Likelihood Estimation for Multiple-Source Loss Tomography With Network Coding
Loss tomography aims at inferring the loss rate of links in a network from end-to-end measurements. Previous work in has developed optimal Maximum Likelihood Estimators (MLEs) for link loss rates in a single-source multicast tree. However, only sub-optimal algorithms have been developed for multiple-source loss tomography. In this paper, the authors revisit multiple-source loss tomography in tree networks with multicast and network coding capabilities, and they provide, for the first time, low-complexity MLEs for the link loss rates. They also derive the rate of convergence of the estimators.