Energy-Efficient On-Demand Resource Provisioning in Cloud Radio Access Networks

Qiang Liu, Tao Han, Nirwan Ansari

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

By leveraging the elasticity of cloud computing, cloud radio access network (C-RAN) facilitates flexible resource management and is one of the key techniques of enabling 5G. In this paper, we study the energy-efficient on-demand resource provisioning in C-RAN by dynamically provisioning the radio and computing resources according to network traffic demands. The network energy consumption of C-RAN is minimized by jointly optimizing the cooperative beamforming, remote radio head (RRH) selection, and virtual baseband units (vBBUs) provisioning. It is challenging to resolve the optimization problem because of the interdependence between the RRH selection and vBBU provisioning. We propose the energy-efficient on-demand C-RAN virtualization (REACT) algorithm to solve the problem in two steps. First, we cluster RRHs into groups by using the hierarchical clustering analysis (HCA) algorithm and assign a vBBU to each RRH group for the baseband signal processing. Second, we determine the RRH selection by optimizing the cooperative beamforming. The performance of the proposed algorithm is validated through extensive simulations, which show that the proposed algorithm significantly reduces the network energy consumption.

Original languageEnglish (US)
Article number8755329
Pages (from-to)1142-1151
Number of pages10
JournalIEEE Transactions on Green Communications and Networking
Volume3
Issue number4
DOIs
StatePublished - Dec 2019

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Computer Networks and Communications

Keywords

  • C-RAN
  • RRH clustering
  • cooperative beamforming
  • energy efficiency
  • resource provisioning

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