cles, and the rates by which the traffic process changes form one state to another state are shown upon the arcs of arrows between the states. The transitions of the states only occur between the neighboring states. Assuming that the system is in statistical equilibrium [Ive04], the system resides in state i with the probability p(i) at a random point of time. When the process is in state i, in case of state transition, it jumps to state (i+1) with 2 times per time unit and to state (i-1) with ill times per time unit. State probabilities State probability p(i) defines the probability of observing the system with i number of busy channels at a random point of time. Knowing the state probabilities is essential to evaluate the system performances such as carried traffic, lost traffic, and utilization of the channels. The state probabilities can be obtained by calculating the one-dimensional Markov chain shown in Figure 6.1. Although, for the state probabilities calculation, exponential service time distribution is assumed, it has been proven that the result is also valid for generally (arbitrarily) distributed service times (see [Kle75]), i.e. M/G/n loss system. The calculation of state probabilities requires to set up the equations describing the states of the system under the assumption of statistical equilibrium. There are two ways to set up the equations: 6 Dimensioning for Single ATM-Based lub a. Global balance: in statistical equilibrium, the transition rate out of a state equals the transition rate into this state. a p(0) = = u. p(1) (i=0) (6.2) a.p(i-1)+(i+1)..p(i+1)=(2+i).p(i (i i>0) b. Local balance: a fictitious cut is put between the states in (i-1) and i, then in statistical equilibrium the transition rate from state (i-1) to i is equal to the transition rate from state i to (i-1). l.p(i-1)=iu.p(i) (i=1,2,....n) (6.3) As the system is always in one of the states, the normalization condition is applied: p(i)=1, (p(i)>0) (6.4) It is noted that the equation (6.2) involves three state probabilities, whereas