Research Article
Outage Probability Analysis for UAV-Aided Mobile Edge Computing Networks
@ARTICLE{10.4108/eetinis.v9i31.960, author={Jun Liu and Jing Wang and Kai Chen and Sunli Feng and Dahua Fan and Jianghong Ou and Fusheng Zhu and Liming Chen and Wen Zhou and Zhusong Liu}, title={Outage Probability Analysis for UAV-Aided Mobile Edge Computing Networks}, journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems}, volume={9}, number={31}, publisher={EAI}, journal_a={INIS}, year={2022}, month={6}, keywords={UAV, mobile edge computing, outage probability, latency}, doi={10.4108/eetinis.v9i31.960} }
- Jun Liu
Jing Wang
Kai Chen
Sunli Feng
Dahua Fan
Jianghong Ou
Fusheng Zhu
Liming Chen
Wen Zhou
Zhusong Liu
Year: 2022
Outage Probability Analysis for UAV-Aided Mobile Edge Computing Networks
INIS
EAI
DOI: 10.4108/eetinis.v9i31.960
Abstract
This paper studies one typical mobile edge computing (MEC) system, where a single user has some intensively calculating tasks to be computed by M edge nodes (ENs) with much more powerful calculating capability. In particular, unmanned aerial vehicle (UAV) can act as the ENs due to its flexibility and high mobility in the deployment. For this system, we propose several EN selection criteria to improve the system whole performance of computation and communication. Specifically, criterion I selects the best EN based on maximizing the received signal-to-noise ratio (SNR) at the EN, criterion II performs the selection according to the most powerful calculating capability, while criterion III chooses one EN randomly. For each EN selection criterion, we perform the system performance evaluation by analyzing outage probability (OP) through deriving some analytical expressions. From these expressions, we can obtain some meaningful insights regarding how to design the MEC system. We finally perform some simulation results to demonstrate the effectiveness of the proposed MEC network. In particular, criterion I can exploit the full diversity order equal to M.
Copyright © 2022 Jun Liu et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.