TY - GEN
T1 - Joint resource segmentation and transmission rate adaptation in Cloud RAN with Caching as a Service
AU - Tang, Jianhua
AU - Quek, Tony Q.S.
AU - Tay, Wee Peng
N1 - Publisher Copyright:
© 2016 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016/8/9
Y1 - 2016/8/9
N2 - By introducing Caching as a Service (CaaS) in Cloud radio access network (C-RAN), the joint resource segmentation and transmission rate adaptation problem is investigated in this paper. Specifically, in the baseband unit (BBU) pool of C-RAN, we optimally segment computation and storage resources to different types of virtual machines (VMs), and in the remote radio heads (RRHs), we adjust the beamformers to obtain the cache-based adaptive rate (CBAR). We aim to minimize the system cost, which includes server cost, VM cost and wireless transmission cost. The joint optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem, which contains l0-norm terms in the objective function and nonconvex constraints. We propose a three-step solution approach, i.e., a general smooth function approximation step, a weighted minimum mean square error (WMMSE) reformulation step and an integer recovery step. Simulation results show that our proposed integer recovery algorithms recover the integer variable values effectively.
AB - By introducing Caching as a Service (CaaS) in Cloud radio access network (C-RAN), the joint resource segmentation and transmission rate adaptation problem is investigated in this paper. Specifically, in the baseband unit (BBU) pool of C-RAN, we optimally segment computation and storage resources to different types of virtual machines (VMs), and in the remote radio heads (RRHs), we adjust the beamformers to obtain the cache-based adaptive rate (CBAR). We aim to minimize the system cost, which includes server cost, VM cost and wireless transmission cost. The joint optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem, which contains l0-norm terms in the objective function and nonconvex constraints. We propose a three-step solution approach, i.e., a general smooth function approximation step, a weighted minimum mean square error (WMMSE) reformulation step and an integer recovery step. Simulation results show that our proposed integer recovery algorithms recover the integer variable values effectively.
UR - http://www.scopus.com/inward/record.url?scp=84984604039&partnerID=8YFLogxK
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U2 - 10.1109/SPAWC.2016.7536886
DO - 10.1109/SPAWC.2016.7536886
M3 - Conference contribution
AN - SCOPUS:84984604039
T3 - IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
BT - SPAWC 2016 - 17th IEEE International Workshop on Signal Processing Advances in Wireless Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2016
Y2 - 3 July 2016 through 6 July 2016
ER -