Multiservice Loss Models for Cloud Radio Access Networks

Abstract

In this paper, a cloud radio access network (C-RAN) is considered where the remote radio heads (RRHs) are separated from the baseband units which form a common pool of computational resource units. Depending on their capacity, the RRHs may form one or more clusters. Each RRH accommodates multiservice traffic, i.e., calls from different service-classes with different radio and computational resource requirements. Arriving calls follow a Poisson process and simultaneously require radio and computational resource units in order to be accepted in the serving RRH. If their resource requirements cannot be met then calls are blocked and lost. Otherwise, calls remain in the serving RRH for a generally distributed service time. Assuming the single-cluster C-RAN, we model it as a multiservice loss system, prove that a product form solution exists for the steady-state probabilities and determine call blocking probabilities via an efficient convolution algorithm whose accuracy is validated via simulation. Furthermore, we generalize the previous multiservice loss model by considering the more complex multi-cluster case where RRHs of the same capacity are grouped in different clusters.

Multiservice Loss Models in C-RAN Supporting Compound Poisson Traffic

“…More specifically, they require a single RRU (from the RRH that will serve them) and a single CRU from the BBU pool. However, in contemporary networks it is essential, for network planning, to consider a multiservice environment where MUs generate calls of various resource requirements [34][35][36][37][38][39][40][41][42][43][44][45][46]. In [45], we introduced two such multirate loss models for the C-RAN architecture and named them multi-class single-cluster and multiclass multi-cluster models (MC-SC and MC-MC, respectively).…”

“…However, in contemporary networks it is essential, for network planning, to consider a multiservice environment where MUs generate calls of various resource requirements [34][35][36][37][38][39][40][41][42][43][44][45][46]. In [45], we introduced two such multirate loss models for the C-RAN architecture and named them multi-class single-cluster and multiclass multi-cluster models (MC-SC and MC-MC, respectively). In [46], we extended both models by assuming that a finite number of MUs has the responsibility to generate traffic towards the RRHs.…”

Multiservice Loss Models in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic

“…A candidate application is the enhanced mobile broadband case which considers service classes with high resource requirements such as virtual reality and online 4K video [44,45]. The proposed work is the first that studies a C-RAN that accommodates multiservice quasi-random traffic and, at the same time, provides convolution algorithms for the efficient determination of congestion probabilities (recently, the case of C-RAN multi-service random traffic has been proposed in [46]). Such algorithms are used in the literature in order to express complicated resource sharing policies such as the bandwidth reservation policy and threshold-based policies [47][48][49][50][51][52][53][54].…”