An Analytical Framework of a C-RAN Supporting Bursty Traffic

In this paper we consider a cloud radio access network (C-RAN) architecture 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 centralized pool of data center resources. Each RRH of the C-RAN accommodates bursty traffic which is expected to play a dominant role in 5G networks. We approximate bursty traffic via the compound Poisson process according to which batches of calls, with a generally distributed batch size, occur at time points that follow a negative exponential distribution. Each call of a new batch is treated separately from the other calls of the same batch. A new call requires a computational resource unit from the centralized pool of BBUs and a radio resource unit from the serving RRH. If both units are available, then the call is accepted in the RRH for an exponentially distributed service time. Otherwise, call blocking occurs. We model this C-RAN as a loss system and show that the steady state probabilities have a product form solution (PFS). Based on the PFS, we propose an efficient convolution algorithm for the accurate calculation of the main performance measures which are time and call congestion probabilities. The accuracy of this algorithm is verified via simulation.