Maximum Likelihood Estimation for Multiple-Source Loss Tomography with Network Coding

摘要：

Loss tomography aims at inferring the loss rate of links in a network from end-to-end measurements. Previous work in 1 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 2-5. In this paper, we revisit multiple-source loss tomography in tree networks with multicast and network coding capabilities, and we provide, for the first time, low-complexity MLEs for the link loss rates. We also derive the rate of convergence of the estimators.

作者: Pegah Sattari Athina Markopoulou Christina Fragouli

作者单位: EECS Department, University of California, Irvine, CA, USA School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland

会议类型: 国际会议

会议名称: 2011 International Symposium on Network Coding(2011网络编码国际会议 NETCOD 2011)

会议地点: 北京

会议语种:英文

页码: 1-7

在线出版日期: 2011-07-25（万方平台首次上网日期，不代表论文的发表时间）