Abstract
The Markov modulated Poisson process (MMPP) has been proposed as a suitable model for characterizing the input traffic to a statistical multiplexer [6]. This paper describes a novel method of parameter estimation for MMPPs. The idea is to employ time discretization to convert an MMPP from the continuous-time domain into the discrete-time domain and then to use a powerful statistical inference technique, known as the EM algorithm, to obtain maximum-likelihood estimates of the model parameters. Tests conducted through a series of simulation experiments indicate that the new method yields results that are significantly more accurate compared to the method described in [8]. In addition, the new method is more flexible and general in that it is applicable to MMPPs with any number of states while retaining nearly constant simplicity in its implementation. Detailed experimental results on the sensitivity of the estimation accuracy to (1) the initialization of the model, (2) the size of the observation interarrival interval data available for the estimation, and (3) the inherent separability of the MMPP states are presented.
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Deng, L., Mark, J.W. Parameter estimation for Markov modulated Poisson processes via the EM algorithm with time discretization. Telecommunication Systems 1, 321–338 (1993). https://doi.org/10.1007/BF02136167
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DOI: https://doi.org/10.1007/BF02136167