On the Statistical Multiplexing Gain of Virtual Base Station Pools

TL;DR

This paper develops a mathematical model to analyze the statistical multiplexing gain of virtual base station pools in cloud radio access networks, highlighting how pooling improves resource utilization and the impact of various parameters.

Contribution

It introduces a multi-dimensional Markov model capturing session dynamics and constraints, providing a recursive method for calculating blocking probabilities and resource utilization limits.

Findings

VBS pools achieve significant pooling gain at medium sizes.

Convergence to large pool limits is slow due to diminishing marginal gains.

Traffic load and QoS significantly influence pool performance.

Abstract

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.