TY - JOUR
T1 - Residual Energy Maximization for Wireless Powered Mobile Edge Computing Systems With Mixed-Offloading
AU - Wu, Mengru
AU - Qi, Weijing
AU - Park, Junhee
AU - Lin, Peng
AU - Guo, Lei
AU - Lee, Inkyu
N1 - Funding Information:
This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFE0206800, in part by the National Natural Science Foundation of China under Grant 62025105, in part by the Chongqing Municipal Education Commission under Grant CXQT21019, in part by the Natural Science Foundation of Chongqing under Grant cstc2020jcyjmsxmX0918, and in part by the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT), Korea Government under Grant 2017R1A2B3012316.
Publisher Copyright:
© 1967-2012 IEEE.
PY - 2022/4/1
Y1 - 2022/4/1
N2 - This paper studies a joint design of resource allocation and task offloading in a wireless powered mobile edge computing network involving different types of computation tasks. To deal with diverse computation tasks, we explore a mixed-offloading paradigm to support the coexistence of partial and binary offloading modes. Specifically, devices harvest energy from an access point (AP) via wireless power transfer (WPT) and utilize the harvested energy to execute their computation tasks using partial or binary offloading. Based on a practical non-linear energy harvesting model, a residual energy maximization problem is formulated by jointly optimizing the transmit power of the AP, the offloading power of devices, the time allocation on WPT and task offloading, and the task partitions and the binary offloading decisions of devices, which turn out to be a non-convex mixed-integer non-linear programming problem. Thus, we develop an efficient dual-layer optimization algorithm by decomposing the optimization problem into an inner and outer layer structure that aims to obtain resource allocation and offloading decisions. Simulation results show that our proposed scheme achieves residual energy gains compared to existing schemes.
AB - This paper studies a joint design of resource allocation and task offloading in a wireless powered mobile edge computing network involving different types of computation tasks. To deal with diverse computation tasks, we explore a mixed-offloading paradigm to support the coexistence of partial and binary offloading modes. Specifically, devices harvest energy from an access point (AP) via wireless power transfer (WPT) and utilize the harvested energy to execute their computation tasks using partial or binary offloading. Based on a practical non-linear energy harvesting model, a residual energy maximization problem is formulated by jointly optimizing the transmit power of the AP, the offloading power of devices, the time allocation on WPT and task offloading, and the task partitions and the binary offloading decisions of devices, which turn out to be a non-convex mixed-integer non-linear programming problem. Thus, we develop an efficient dual-layer optimization algorithm by decomposing the optimization problem into an inner and outer layer structure that aims to obtain resource allocation and offloading decisions. Simulation results show that our proposed scheme achieves residual energy gains compared to existing schemes.
KW - Binary offloading
KW - mobile edge computing
KW - partial offloading
KW - resource allocation
KW - wireless power transfer
UR - http://www.scopus.com/inward/record.url?scp=85124196822&partnerID=8YFLogxK
U2 - 10.1109/TVT.2022.3147824
DO - 10.1109/TVT.2022.3147824
M3 - Article
AN - SCOPUS:85124196822
SN - 0018-9545
VL - 71
SP - 4523
EP - 4528
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 4
ER -