Joint Communication and Computing Resource Allocation in 5G Cloud Radio Access Networks

Abstract

Cloud-radio access network (C-RAN) is regarded as a promising solution to manage heterogeneity and scalability of future wireless networks. The centralized cooperative resource allocation and interference cancellation methods in C-RAN significantly reduce the interference levels to provide high data rates. However, the centralized solution is not scalable due to the dense deployment of small cells with fractional frequency reuse, causing severe inter-tier and inter-cell interference turning the resource allocation and user association into a more challenging problem. In this paper, we investigate joint communication and computing resource allocation along with user association, and baseband unit (BBU) and remote radio head (RRH) mapping in C-RANs. We initially establish a queueing model in C-RAN, followed by formulation of two optimization problems for communication [e.g., resource blocks (RBs) and power] and computing [e.g., virtual machines (VMs)] resources allocation with the aim to minimize mean response time. User association along with the RB allocation, interference, and queueing stability constraints are considered in the communication resource optimization problem. The computing resource optimization problem considers BBU-RRH mapping and VM allocation for small cells, constrained to BBU server capacity and queueing stability. To solve the communication and computing resource optimization problem, we propose a joint resource allocation solution that considers a double-sided auction based distributed resource allocation (DS-ADRA) method, where small cell base stations and users jointly participate using the concept of auction theory. The proposed method is evaluated via simulations by considering the effect of bandwidth utilization percentage, signal-to-interference ratio threshold value and the number of users. The results show that the proposed method can be successfully implemented for 5G C-RANs.