blocking in large mobile cellular networks with bursty traffic
We consider large cellular networks. The traffic entering the network is assumed to be correlated in both space and time. The space dependency captures the possible correlation between the arrivals to different nodes in the network, while the time dependency captures the time correlation between arrivals to each node. We model such traffic with a Markov-Modulated Poisson Process(MMPP).It is shown that even in the single node environment, the problem is not mathematically tractable. A model with an infinite number of circuits is used to approximate the finite model. A novel recursive methodology is introduced in finding the joint moments of the number of busy circuits in different cells in the network leading to accurate determination of blocking probability. A simple mixed-Poisson distribution is introduced as an accurate approximation of the distribution of the number ofbusy circuits.We show that for certain cases, in the system with an infinite number of circuits in each cell, there is no effect of mobility on the performance of the system. Our numerical results indicate that the traffic burstiness has a major impact on the system performance. The mixed-Poisson approximation is found to be a very good fit to the exact finite model. The performance of this approximation using few moments is affected by traffic burstiness and average load. We find that in a reasonable range of traffic burstiness, the mixed-Poisson distribution provides a close approximation.
mobility cellular network network traffic correlation space timing dependencies model environments circuits approximation recursion methodology determinism probability distributed systems effect performance fit process