Open AccessJoint optimisation of UAV grouping and energy consumption in MEC‐enabled UAV communication networks
Author(s) -
Zhu Zhengying,
Qian Li Ping,
Shen Jiafang,
Huang Liang,
Wu Yuan
Publication year - 2020
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2019.1179
Subject(s) - computer science , computation offloading , resource allocation , mathematical optimization , energy consumption , transmitter power output , computation , base station , simulated annealing , mobile edge computing , distributed computing , enhanced data rates for gsm evolution , edge computing , computer network , algorithm , channel (broadcasting) , telecommunications , transmitter , mathematics , engineering , electrical engineering
This study presents a mobile edge computing (MEC)‐enabled UAV communication system, where a number of UAVs are served by terrestrial base stations (TBSs) equipped with computation resource in the non‐orthogonal multiple access manner. Each UAV has to offload its computing tasks to the proper TBS due to the limited energy supply. For this, the authors aim at minimising the sum of transmission energy of UAVs and computation energy of TBSs through jointly optimising the UAV transmit power, computation resource allocation, and UAV grouping. Considering the non‐convexity of this optimisation problem, they obtain the optimal solution in the coupled steps: the convex resource allocation optimisation and the combinatorial UAV grouping optimisation. By exploiting the convex nature of the resource allocation optimisation problem, they obtain the optimal transmit power and computation allocation based on the KKT conditions and the idea of gradient descent method when considering a single TBS. Then, they adopt the simulated annealing to obtain the optimal UAV grouping and TBS selection based on the proposed resource allocation optimisation algorithm. Finally, simulation results show that the proposed joint optimisation of transmit power, computation resource allocation, and UAV grouping can effectively reduce the energy consumption of MEC‐aware UAV communication system.