Hidden Markov modeling for network communication channels

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.