TY - JOUR
T1 - On the Design of Computation Offloading in Fog Radio Access Networks
AU - Zhao, Zhongyuan
AU - Bu, Shuqing
AU - Zhao, Tiezhu
AU - Yin, Zhenping
AU - Peng, Mugen
AU - Ding, Zhiguo
AU - Quek, Tony Q.S.
N1 - Funding Information:
The work of Z. Zhao was supported in part by Beijing Natural Science Foundation under Grant L182039 and in part by National Science and Technology Major Project underGrant 2017ZX03001014. Thework of Z.Dingwas supported in part byUKEPSRC underGrant EP/N005597/2, in part by NSFC under Grant 61728101, and in part by H2020-MSCA-RISE-2015 under Grant 690750. Thework of T.Q. S.Quekwas supported by the SUTD-ZJUResearch Collaboration underGrants SUTD-ZJU/RES/01/2016 and SUTD-ZJU/RES/05/2016.
Funding Information:
Manuscript received September 17, 2018; revised February 23, 2019; accepted May 19, 2019. Date of publication June 4, 2019; date of current version July 16, 2019. The work of Z. Zhao was supported in part by Beijing Natural Science Foundation under Grant L182039 and in part by National Science and Technology Major Project under Grant 2017ZX03001014. The work of Z. Ding was supported in part by UK EPSRC under Grant EP/N005597/2, in part by NSFC under Grant 61728101, and in part by H2020-MSCA-RISE-2015 under Grant 690750. The work of T. Q. S. Quek was supported by the SUTD-ZJU Research Collaboration under Grants SUTD-ZJU/RES/01/2016 and SUTD-ZJU/RES/05/2016. The review of this paper was coordinated by Dr. S. Misra. (Corresponding author: Zhongyuan Zhao.) Z. Zhao, S. Bu, and M. Peng are with the Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing, University of Posts and Telecommunications, Beijing 100876, China (e-mail: [email protected]; [email protected]; [email protected]).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Based on a hierarchical cloud-fog computing-enabled paradigm, fog radio access networks (F-RANs) can provide abundant resource to support the future mobile artificial intelligent services. However, due to the differences of computation and communication capabilities at the cloud computing center, the fog computing based access points (F-APs), and the user devices, it is challenging to propose efficient computation offloading strategies to fully explore the potential of F-RANs. In this paper, we study the design of computation offloading in F-RANs to minimize the total cost with respect to the energy consumption and the offloading latency. In particular, a joint optimization problem is formulated to optimize the offloading decision, the computation and the radio resources allocation. To solve this non-linear and non-convex problem, an iterative algorithm is designed, which can be proved to converge a stationary optimal solution with polynomial computational complexity. Finally, the simulation results are provided to show the performance gains of our proposed joint optimization algorithm.
AB - Based on a hierarchical cloud-fog computing-enabled paradigm, fog radio access networks (F-RANs) can provide abundant resource to support the future mobile artificial intelligent services. However, due to the differences of computation and communication capabilities at the cloud computing center, the fog computing based access points (F-APs), and the user devices, it is challenging to propose efficient computation offloading strategies to fully explore the potential of F-RANs. In this paper, we study the design of computation offloading in F-RANs to minimize the total cost with respect to the energy consumption and the offloading latency. In particular, a joint optimization problem is formulated to optimize the offloading decision, the computation and the radio resources allocation. To solve this non-linear and non-convex problem, an iterative algorithm is designed, which can be proved to converge a stationary optimal solution with polynomial computational complexity. Finally, the simulation results are provided to show the performance gains of our proposed joint optimization algorithm.
KW - Computation offloading
KW - fog radio access networks
KW - resource allocation
UR - http://www.scopus.com/inward/record.url?scp=85069431310&partnerID=8YFLogxK
U2 - 10.1109/TVT.2019.2919915
DO - 10.1109/TVT.2019.2919915
M3 - Article
AN - SCOPUS:85069431310
SN - 0018-9545
VL - 68
SP - 7136
EP - 7149
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 7
M1 - 8730522
ER -