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UAV-Assisted Relaying and Edge Computing: Scheduling and Trajectory Optimization

Hu, X; Wong, K-K; Yang, K; Zheng, Z; (2019) UAV-Assisted Relaying and Edge Computing: Scheduling and Trajectory Optimization. IEEE Transactions on Wireless Communications , 18 (10) pp. 4738-4752. 10.1109/twc.2019.2928539. Green open access

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Abstract

In this paper, we study an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) architecture, in which a UAV roaming around the area may serve as a computing server to help user equipment (UEs) compute their tasks or act as a relay for further offloading their computation tasks to the access point (AP). We aim to minimize the weighted sum energy consumption of the UAV and UEs subject to the task constraints, the information-causality constraints, the bandwidth allocation constraints and the UAV’s trajectory constraints. The required optimization is nonconvex, and an alternating optimization algorithm is proposed to jointly optimize the computation resource scheduling, bandwidth allocation, and the UAV’s trajectory in an iterative fashion. The numerical results demonstrate that significant performance gain is obtained over conventional methods. Also, the advantages of the proposed algorithm are more prominent when handling computation-intensive latency-critical tasks.

Type: Article
Title: UAV-Assisted Relaying and Edge Computing: Scheduling and Trajectory Optimization
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/twc.2019.2928539
Publisher version: https://doi.org/10.1109/twc.2019.2928539
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10085354
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