N etwork-C onnected UAV C ommunications
太乐网An Energy Efficient Design for UAV Communication With Mobile Edge Computing
Lingyan Fan*1, Wu Yan1, Xihan Chen2* , Zhiyong Chen3, Qingjiang Shi4
1 Deparment of Communication Engineering, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China
2 College of ISEE, Zhejiang University, Hangzhou, Zhejiang 310000, China
3 Deparment of Communication Engineering, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
4 School of Software Engineering, Tongji University, Shanghai 200092, China
* The corresponding author, email: chenxihan@zju.edu
Received: Feb. 12,2018 Revised: Jun. 22,2018 Editor: Yong Zeng Abstract: This paper considers a UAV com
munication system w ith mobile edge comput
山东省
人口与计划生育
条例修正案
ing (MEC). We minimize the energy consump
tion o f the whole system via jo in tly optimizing
the U AV’s trajectory and task assignment as
w ell as CPU’s computational speed under the
set o f resource constrains. To this end, we first
derive the energy consumption model of data
processing, and then obtain the energy con
sumption model o f fixed-w ing U AV’s flig h t.
The optim ization problem is mathematically
formulated. To address the problem, we first
obtain the approximate optim ization problem
by applying the technique o f discrete linear
state-space approximation, and then transform
the non-convex constraints into convex by us
菲达协同ing linearization. Furthermore, a concave-con
vex procedure (CCCP) based algorithm is
proposed in order to solve the optim ization
problem approxim ately. N um erical results
show the efficacy o f the proposed algorithm.
Keywords: mobile edge computing (MEC);
UAV communication; concave-convex proce
dure (CCCP); energy minimization
I. I ntroduction
In the past few years, the technology o f ma
chine learning, virtual reality (VR), augmented
reality (AR) and so on has developed rapidly
and has made great progress. A t the same time,
these technologies have also raised higher rebeijingreview
quirements for the ability o f computation and
radio resources o f the devices [1]. As we all
know, computation capacity and battery life o f
mobile devices have been improved slowly, so
the quality o f user experience (QoUE) is d iffi
cult to guarantee. To counter these problems,烛之武
mobile edge computing (MEC) is proposed as
a very promising concept in order to promote
the network performances as w ell as the user
experience by offloading the computation tasks
from the mobile device to the MEC server with
powerful computational capabilities [2].
Recently, a number o f studies have been
辽宁医学院护理学院
proposed fo r MEC. The work [3] considered an energy-aware offloading scheme, which
jo in tly optim izes communication and com
putation resource allocation under the lim ited
energy and sensitive latency. In [4], the author
investigated a jo in t sub-carrier and CPU time
allocation algorithm fo r a MEC system, which
significantly outperform s the conventional
algorithm. The work [5] designed a computa
tion offloading and data caching model under
the jo in t o f MEC and data center (DC), then
formulate a resource-constrained delay m ini
mization problem, and put forward a heuristic
26China Communications • January 2019
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