TY - JOUR

T1 - Hidden Markov modeling for network communication channels.

AU - Salamatian, K.

AU - Vaton, S.

N1 - This paper develops a model of packet losses as observed over an Internet connection. It is known in the community as the first paper that democratizes the use of advanced statistical methods such as Hidden Markov Models and more generally latent variables methods in the Internet measurement community. As these methods are now essential, this paper is very frequently used, to the authors' knowledge at least in 23 courses worldwide, as a reference for graduate students studying Internet modelling. The paper has 40 citations on Google Scholar. RAE_import_type : Journal article RAE_uoa_type : Computer Science and Informatics

PY - 2001/6

Y1 - 2001/6

N2 - In this paper we perform the statistical analysis of an Internet communication channel. Our study is based on a Hidden Markov Model (HMM). The channel switches between different states; to each state corresponds the probability that a packet sent by the transmitter will be lost. The transition between the different states of the channel is governed by a Markov chain; this Markov chain is not observed directly, but the received packet flow provides some probabilistic information about the current state of the channel, as well as some information about the parameters of the model. In this paper we detail some useful algorithms for the estimation of the channel parameters, and for making inference about the state of the channel. We discuss the relevance of the Markov model of the channel; we also discuss how many states are required to pertinently model a real communication channel.

AB - In this paper we perform the statistical analysis of an Internet communication channel. Our study is based on a Hidden Markov Model (HMM). The channel switches between different states; to each state corresponds the probability that a packet sent by the transmitter will be lost. The transition between the different states of the channel is governed by a Markov chain; this Markov chain is not observed directly, but the received packet flow provides some probabilistic information about the current state of the channel, as well as some information about the parameters of the model. In this paper we detail some useful algorithms for the estimation of the channel parameters, and for making inference about the state of the channel. We discuss the relevance of the Markov model of the channel; we also discuss how many states are required to pertinently model a real communication channel.

U2 - 10.1145/384268.378439

DO - 10.1145/384268.378439

M3 - Journal article

VL - 29

SP - 92

EP - 101

JO - ACM SIGMETRICS Performance Evaluation Review

JF - ACM SIGMETRICS Performance Evaluation Review

IS - 1

ER -