Pubblicazione scientifica

Mobile Edge Computing Empowered Energy Efficient Task Offloading in 5G

FILONE TECNOLOGICO	TEMA
trasformazione e commercializzazione

RIASSUNTO

Mobile edge computing has been proposed in recent years to offload computation tasks from user equipments (UEs) to the network edge to break hardware limitations and resource constraints at UEs. Although there have been some existing works on computation offloading in 5G, most of them fail to take into account the unique property of 5G in their scheme design. In this paper, we consider small-cell network architecture for task offloading. In order to achieve energy efficiency, we model the energy consumption of offloading from both task computation and communication aspects. Besides, transmission scheduling are carried over both the fronthaul and backhaul links. We first formulate an energy optimization problem of offloading, which aims at minimizing the overall energy consumption at all system entities and takes into account of the constraints from both computation capabilities and service delay requirement. We then develop an artificial fish swarm algorithm based scheme to solve the energy optimization problem. Besides, the global convergence property of the our scheme is formally proven. Finally, various simulation results demonstrate the efficiency of our scheme.

DATA

Data di pubblicazione: 29/01/2018

AUTORI

LICHAO YANG
HELI ZHANG
MING LI
JUN GUO
HONG JI

ENTE DI AFFERENZA

KEY LAB OF UNIVERSAL WIRELESS COMM MINST EDUCATION BEIJING UNIV POSTS & TELECOM BEIJING CHINA
DEPT COMPUTER SCIENCE & ENG UNIV NEVADA RENO USA

RIVISTA

IEEE Transactions on Vehicular Technology (Volume: 67, Issue: 7, Page(s): 6398-6409)

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