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The Markov modulated batch Poisson process generalizes the compound Poisson process by allowing for correlated interarrival times This process is used extensively in the analysis of teletraf c models (a special case is the composite model of independent on-off sources multiplexed together) A so-called phase process underlies the arrival process, where the evolution of the phase process occurs isolated from the arrivals The phase process can only assume a nite number of states i = 1, , m The sojourn time of the phase process in state i is exponentially distributed with mean 1/ i If the phase process leaves state i, it goes to state j with probability pij , independently of the duration of the stay in state i It is assumed that pii = 0 for all i The arrival process of customers is a compound Poisson process whose parameters depend on the state of the phase process If the phase process is in state i, then batches of customers arrive according to a Poisson process with rate i where the batch size has the

(i) discrete probability distribution {ak , k = 1, 2, } It is no restriction to assume (i) (i) (i) (i) (i) that a0 = 0; otherwise replace i by i (1 a0 ) and ak by ak /(1 a0 ) for k 1 For any t 0 and i, j = 1, , m, de ne

Pij (k, t) = P {the total number of customers arriving in (0, t) equals k and the phase process is in state j at time t | the phase process is in state i at the present time 0}, k = 0, 1,

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Also, for any t > 0 and i, j = 1, , m, let us de ne the generating function Pij (z, t) by Pij (z, t) = k=0 To derive an expression for Pij (z, t), it is convenient to use matrix notation Let Q = (qij ) be the m m matrix whose (i, j )th element is given by

The following enhancements are introduced in CAMEL Phase in 3GPP Rel-7

Pij (k, t)zk ,

(b) For each initial state X(0) = i with i = r, the total reward earned until the rst visit of the process {X(t)} to state r is nite with probability 1 This assumption is automatically satis ed when the state space I is nite and Assumption 421 holds For each t > 0, de ne the random variable R(t) by R(t) = the total reward earned up to time t The following very useful result holds for the long-run average reward

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