The decomposition of a blocking model for connection-oriented networks

Two general-purpose decomposition methods to calculate the blocking probabilities of connection-oriented networks are presented. The methods are based on either the call status or the link status of the networks, and can significantly reduce the required computational times. A heuristic is presented to simplify the application of the proposed decomposition methods on networks with irregular topologies. Numerical examples are given to demonstrate the applications of the proposed methods. © 2004 IEEE.published_or_final_versio

On the Statistical Multiplexing Gain of Virtual Base Station Pools

Facing the explosion of mobile data traffic, cloud radio access network (C-RAN) is proposed recently to overcome the efficiency and flexibility problems with the traditional RAN architecture by centralizing baseband processing. However, there lacks a mathematical model to analyze the statistical multiplexing gain from the pooling of virtual base stations (VBSs) so that the expenditure on fronthaul networks can be justified. In this paper, we address this problem by capturing the session-level dynamics of VBS pools with a multi-dimensional Markov model. This model reflects the constraints imposed by both radio resources and computational resources. To evaluate the pooling gain, we derive a product-form solution for the stationary distribution and give a recursive method to calculate the blocking probabilities. For comparison, we also derive the limit of resource utilization ratio as the pool size approaches infinity. Numerical results show that VBS pools can obtain considerable pooling gain readily at medium size, but the convergence to large pool limit is slow because of the quickly diminishing marginal pooling gain. We also find that parameters such as traffic load and desired Quality of Service (QoS) have significant influence on the performance of VBS pools.Comment: Accepted by GlobeCom'1

Performance Evaluation in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic

In this paper, a cloud radio access network (C-RAN) is considered where the remote radio heads (RRHs) are separated from the baseband units (BBUs). The RRHs in the C-RAN are grouped in different clusters according to their capacity while the BBUs form a centralized pool of computational resource units. Each RRH services a finite number of mobile users, i.e., the call arrival process is the quasi-random process. A new call of a single service-class requires a radio and a computational resource unit in order to be accepted in the C-RAN for a generally distributed service time. If these resource units are unavailable, then the call is blocked and lost. To analyze the multi-cluster C-RAN, we model it as a single-rate loss system, show that a product form solution exists for the steady state probabilities and propose a convolution algorithm for the accurate determination of congestion probabilities. The accuracy of this algorithm is verified via simulation. The proposed model generalizes our recent model where the RRHs in the C-RAN are grouped in a single cluster and each RRH accommodates quasi-random traffic

Impacts of buffering voice calls in integrated voice and data services

In this study, we aim to analyse the relationship between various characteristics of a communication system with data and voice call requests. Queuing theory and Markov chain analysis are effectively used for this purpose. Such a study is useful for understanding how the proposed mathematical models behave which represents a system with integrated voice and data calls in homogenous wireless networks. We also propose to optimise the system characteristics in an attempt to provide better Quality of Service (QoS) for systems with integrated voice and data calls. The proposed models have two dimensions; one for voice calls and one for data calls. A channel is assigned for two input traffic call, namely, voice and data calls. The incoming voice and data calls are queued when the channel is busy. Since voice calls are delay-sensitive, priority is given to voice calls. Also, since there is only one channel, data calls are only serviced if there are no voice calls in the system. For such systems, it is important to analyse the impact of buffering the voice calls as well as data calls for various mean rates of call requests, and mean service times. The analytical models presented are generic which is applicable for various systems with similar characteristics. Numerical results are also provided. The results show that the proposed models can be used for optimisation of the performance of a given network