TY - GEN
T1 - Energy-Efficient On-Demand Cloud Radio Access Networks Virtualization
AU - Liu, Qiang
AU - Han, Tao
AU - Ansari, Nirwan
N1 - Funding Information:
This work is partially supported by the U.S. National Science Foundation under Grant No. 1731675, No. 1810174 and the UNC-Charlotte Faculty Research Grant.
Publisher Copyright:
© 2018 IEEE.
PY - 2018
Y1 - 2018
N2 - By leveraging the elasticity of cloud computing, cloud radio access network (C-RAN) facilitates on-demand radio and computing resource provisioning. In this paper, we propose an energy-efficient on-demand C-RAN virtualization model which dynamically provisions virtual C-RAN according to service demand. The energy consumption of the virtual C-RAN is minimized by jointly optimizing the remote radio head (RRH) selection and computing resource provisioning. The network energy consumption minimization problem is challenging because of the interdependence between the RRH selection and the computing resource 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 using the hierarchical clustering analysis (HCA) algorithm and assign a BBU 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 evaluated through extensive simulations, which shows the proposed algorithm reduces up to 62% of the network energy consumption as compared to a baseline algorithm.
AB - By leveraging the elasticity of cloud computing, cloud radio access network (C-RAN) facilitates on-demand radio and computing resource provisioning. In this paper, we propose an energy-efficient on-demand C-RAN virtualization model which dynamically provisions virtual C-RAN according to service demand. The energy consumption of the virtual C-RAN is minimized by jointly optimizing the remote radio head (RRH) selection and computing resource provisioning. The network energy consumption minimization problem is challenging because of the interdependence between the RRH selection and the computing resource 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 using the hierarchical clustering analysis (HCA) algorithm and assign a BBU 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 evaluated through extensive simulations, which shows the proposed algorithm reduces up to 62% of the network energy consumption as compared to a baseline algorithm.
KW - C-RAN
KW - Cooperative beamforming
KW - Energy efficiency
KW - Network virtualization
UR - http://www.scopus.com/inward/record.url?scp=85063516440&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063516440&partnerID=8YFLogxK
U2 - 10.1109/GLOCOM.2018.8647929
DO - 10.1109/GLOCOM.2018.8647929
M3 - Conference contribution
AN - SCOPUS:85063516440
T3 - 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings
BT - 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Global Communications Conference, GLOBECOM 2018
Y2 - 9 December 2018 through 13 December 2018
ER -