A hidden Markov model for Internet channels

Performance of real-time applications on network communication channels are strongly related to losses and temporal delays. Several studies have shown that these network features may be correlated and present a certain degree of memory such as bursty losses and delays. The memory and the statistical dependence between losses and temporal delays suggest that the channel may be well modelled by a hidden Markov model (HMM) with appropriate hidden variables that captures the current state of the network. In this paper we propose an HMM that, trained with a modified version of the EM-algorithm, shows excellent performance in modelling typical channel behaviors in a set of real packet links.