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Hidden Markov Modeling for Network Communication Channels
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Authors
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Kavé Salamatian <Kave.Salamatian@lip6.fr>
Laboratoire LIP6-CNRS UMR7606, Université Pierre et Marie Curie, Paris,
France
Sandrine Vaton <Sandrine.Vaton@enst-bretagne.fr>
ENST Bretagne, Brest, France
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Abstract
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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.
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