an the simple Poisson process or the batch Poisson process, as they can effectively capture dependence and correlation, salient characteristics of the arrival process. 6.4 Dimensioning for Transport Network Performance The BMAP model is characterized by parameters D(0), which represents the transition probability between the transient states without arrivals, and D(m), which is the transition probability within an arrival of packet with batch size m. The BMAP can capture the packet arrival process and packet length process. In previous investigations, the BMAP model has been applied to analyze the aggregated traffic modeling of IP networks [KLL03]. The BMAP model is chosen as the arrival process model as it considers different lengths of packets and batch arrivals. The BMAP model defines D(0) and D(m) (m>1). D(m) represents the packet arrival process. And m corresponds to the different lengths of the packet of measured traffic. For example, if there are three different RAB types in the UMTS network, that means there are three different packet lengths (i.e. FP PDU sizes) corresponding to three different RAB types in the UMTS network. Correspondingly the BMAP will consider three different packet lengths, i.e. FP PDU sizes. In this way BMAP can provide a rather accurate model for characterizing the aggregated traffic, even on the self-similarity and burstiness properties of the IP traffic Normally, the BMAP traffic arrival process is difficult to calculate analytically. Usually, the parameter estimation method is used for estimating the model parameters based on the observations of the packet inter-arrival time and packet size from the measured traffic. There are a few current studies on the parameter estimation for the BMAP model. Ryden has employed the Expectation Maximization (EM) algorithm for the MMPP model [Ryd96]. Breuer and Lindemann developed the method of the EM algorithm for the BMAP [Bre02] [LL01]. In this thesis, the EM for BMAP is implemented based on the above two methods from Ryden and Lindemann. In 