Abstract

In this paper, a cloud radio access network (C-RAN) is considered where the baseband units form a pool of computational resource units and are separated from the remote radio heads (RRHs). Based on their radio capacity, the RRHs may form one or many clusters: a single cluster when all RRHs have the same capacity and multi-clusters where RRHs of the same radio capacity are grouped in the same cluster. Each RRH services the so-called multiservice traffic, i.e., calls from many service classes with various radio and computational resource requirements. Calls arrive in the RRHs according to a quasi-random process. This means that new calls are generated by a finite number of mobile users. Arriving calls require simultaneously computational and radio resource units in order to be accepted in the system, i.e., in the serving RRH. If their requirements are met, then these calls are served in the (serving) RRH for a service time which is generally distributed. Otherwise, call blocking occurs. We start with the single-cluster C-RAN and model it as a multiservice loss system, prove that the model has a product form solution, and determine time congestion probabilities via a convolution algorithm whose accuracy is validated with the aid of simulation. Furthermore, the previous model is generalized to include the more complex case of more than one clusters.

Highlights

  • The w-th remote radio heads (RRHs) (w = 1, . . . , 6) serves Sw service classes and let bw,s = bwc,s = bwr,s be the resource units (RUs) required by a service class s call

  • Two new multi-rate loss models are proposed in this work, namely the finite multi-class-single-cluster (f-MC-SC) and the finite multiclass-multi-cluster (f-MC-MC) models, for the analysis of a cloud radio access network (C-RAN) that accommodates different service classes of calls which arrive in the system according to a quasi-random process

  • The f-MCSC model describes a C-RAN of a single cluster of RRHs while the f-MC-MC model includes multiple clusters of RRHs

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Summary

Introduction

The cloud radio access network (C-RAN) architecture is considered to be a promising and, at the same time, cost-effective solution to face the increasing wireless traffic as well as the soaring demand for enhanced data rate and decreased latency [1,2]. 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].

The Analytical Model
TC Probabilities via the Proposed BF Method
TC Probabilities via the Proposed Convolution Algorithm
Evaluation
The Convolution Algorithm for the Computation of TC Probabilities
Conclusions
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