An Analytical Framework of a C-RAN Supporting Random, Quasi-Random and Bursty Traffic

We consider a cloud radio access network (C-RAN) where the baseband signal processing servers, named baseband units (BBUs) are separated from the remote radio heads (RRHs). The RRHs form a single cluster while the BBUs form a pool of resources. Each RRH may accommodate random (Poisson) or quasi-random or bursty traffic. The latter is approximated via the compound Poisson process according to which batches of calls, with a generally distributed batch size, follow a Poisson process. A call requires a computational resource and a radio resource unit from the BBUs and the serving RRH, respectively. If any of the two units is unavailable, call blocking occurs. Otherwise, the new call is accepted in the RRH. We model this C-RAN as a loss system and study two different cases: i) all RRHs accommodate bursty traffic and ii) some RRHs accommodate random traffic, some quasi-random traffic and the rest RRHs accommodate bursty traffic. In both cases, we show that a product form solution exists for the steady state probabilities and propose efficient convolution algorithms for the accurate calculation of time and call congestion probabilities. The accuracy of these algorithms is verified via simulation.