TY - GEN
T1 - Cross-layer resource allocation in cloud radio access network
AU - Tang, Jianhua
AU - Tay, Wee Peng
AU - Quek, Tony Q.S.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/2/5
Y1 - 2014/2/5
N2 - Cloud radio access network (C-RAN) aims to improve the spectrum and energy efficiency of wireless communication networks by migrating conventional distributed base station functionalities into a centralized cloud baseband unit (BBU) pool. We investigate a cross-layer resource allocation model for C-RAN to minimize the overall system power consumption in both the BBU pool and the remote radio heads (RRHs), while guaranteeing the cross-layer QoS. We characterize the cross-layer resource allocation problem as a mixed-integer nonlinear programming (MINLP), which is however NP-hard. By relaxing the original MINLP problem to a quasi weighted sum-rate maximization (QWSRM) problem, we utilize a branch and bound method to solve the QWSRM problem, and propose a low-complexity bisection search algorithm to obtain a sparse solution for RRH selection problem. Simulation results suggest that our cross-layer approach achieves more energy savings than the recently proposed greedy selection and successive selection algorithms for optimal RRH selection.
AB - Cloud radio access network (C-RAN) aims to improve the spectrum and energy efficiency of wireless communication networks by migrating conventional distributed base station functionalities into a centralized cloud baseband unit (BBU) pool. We investigate a cross-layer resource allocation model for C-RAN to minimize the overall system power consumption in both the BBU pool and the remote radio heads (RRHs), while guaranteeing the cross-layer QoS. We characterize the cross-layer resource allocation problem as a mixed-integer nonlinear programming (MINLP), which is however NP-hard. By relaxing the original MINLP problem to a quasi weighted sum-rate maximization (QWSRM) problem, we utilize a branch and bound method to solve the QWSRM problem, and propose a low-complexity bisection search algorithm to obtain a sparse solution for RRH selection problem. Simulation results suggest that our cross-layer approach achieves more energy savings than the recently proposed greedy selection and successive selection algorithms for optimal RRH selection.
UR - http://www.scopus.com/inward/record.url?scp=84949928255&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84949928255&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP.2014.7032098
DO - 10.1109/GlobalSIP.2014.7032098
M3 - Conference contribution
AN - SCOPUS:84949928255
T3 - 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
SP - 158
EP - 162
BT - 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
Y2 - 3 December 2014 through 5 December 2014
ER -
