Research Article
Energy-Efficient Partial Offloading with Transmission Power Control in Mobile-Edge Computing
@INPROCEEDINGS{10.1007/978-3-030-90196-7_44, author={Meng Wang and Shuo Shi and Jian He and Cong Zhou and Zhong Zheng}, title={Energy-Efficient Partial Offloading with Transmission Power Control in Mobile-Edge Computing}, proceedings={Artificial Intelligence for Communications and Networks. Third EAI International Conference, AICON 2021, Xining, China, October 23--24, 2021, Proceedings, Part I}, proceedings_a={AICON}, year={2021}, month={11}, keywords={Mobile-edge computing Partial computation offloading Convex optimization Transmission power control}, doi={10.1007/978-3-030-90196-7_44} }- Meng Wang
Shuo Shi
Jian He
Cong Zhou
Zhong Zheng
Year: 2021
Energy-Efficient Partial Offloading with Transmission Power Control in Mobile-Edge Computing
AICON
Springer
DOI: 10.1007/978-3-030-90196-7_44
Abstract
Mobile-Edge Computing (MEC) is a promising paradigm which enables mobile devices (MDs) to offload the computation-sensitive tasks (e.g., Augmented Reality) to the MEC server in close proximity to MDs to obtain low execution latency and energy consumption. Different from traditional partial offloading scheme, in this paper, we study the partial offloading in MEC. Consider single user scenario and Rayleigh fading channel, we first formulate the energy-efficient partial offloading problem as a non-convex problem. To solve the problem optimally, we reformulate the problem into two subproblems and use block coordinate descent method to solve the problem separately. For each subproblem, we give the proof of its feasibility. Simulation results show that the proposed method consumes less energy compared with two benchmark algorithms.
