Abstract
Performance of real-time applications on network communication channels are strongly related to losses and temporal delays. Several studies showed 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 Dynamic Bayesian Network with an appropriate hidden variable that captures the current state of the network. In this paper we propose a Bayesian model that, trained with a version of the EM-algorithm, seems to be effective in modelling typical channel behaviors.
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Rossi, P.S., Romano, G., Palmieri, F., Iannello, G. (2003). Bayesian Modelling for Packet Channels. In: Apolloni, B., Marinaro, M., Tagliaferri, R. (eds) Neural Nets. WIRN 2003. Lecture Notes in Computer Science, vol 2859. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45216-4_25
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DOI: https://doi.org/10.1007/978-3-540-45216-4_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20227-1
Online ISBN: 978-3-540-45216-4
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