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Edge Computing Enabled Mobile Augmented Reality with Imperfect Channel Knowledge

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Edge Computing Enabled Mobile Augmented Reality with Imperfect Channel Knowledge. / Liu, Jianhui; Zhang, Qi.
European Wireless 2019 Conference, EW 2019. VDE Verlag GmbH, 2019. s. 90-95.

Publikation: Bidrag til bog/antologi/rapport/proceeding › Konferencebidrag i proceedings › Forskning › peer review

Harvard

Liu, J & Zhang, Q 2019, Edge Computing Enabled Mobile Augmented Reality with Imperfect Channel Knowledge. i European Wireless 2019 Conference, EW 2019. VDE Verlag GmbH, s. 90-95, European Wireless 2019 - 25th European Wireless Conference, Aarhus, Danmark, 02/05/2019. <https://www.vde-verlag.de/proceedings-en/564948016.html>

APA

Liu, J., & Zhang, Q. (2019). Edge Computing Enabled Mobile Augmented Reality with Imperfect Channel Knowledge. I European Wireless 2019 Conference, EW 2019 (s. 90-95). VDE Verlag GmbH. https://www.vde-verlag.de/proceedings-en/564948016.html

CBE

Liu J, Zhang Q. 2019. Edge Computing Enabled Mobile Augmented Reality with Imperfect Channel Knowledge. I European Wireless 2019 Conference, EW 2019. VDE Verlag GmbH. s. 90-95.

MLA

Liu, Jianhui og Qi Zhang "Edge Computing Enabled Mobile Augmented Reality with Imperfect Channel Knowledge". European Wireless 2019 Conference, EW 2019. VDE Verlag GmbH. 2019, 90-95.

Vancouver

Liu J, Zhang Q. Edge Computing Enabled Mobile Augmented Reality with Imperfect Channel Knowledge. I European Wireless 2019 Conference, EW 2019. VDE Verlag GmbH. 2019. s. 90-95

Author

Liu, Jianhui ; Zhang, Qi. / Edge Computing Enabled Mobile Augmented Reality with Imperfect Channel Knowledge. European Wireless 2019 Conference, EW 2019. VDE Verlag GmbH, 2019. s. 90-95

Bibtex

@inproceedings{bac8355714394d11bcc7a7bf787235c8,
title = "Edge Computing Enabled Mobile Augmented Reality with Imperfect Channel Knowledge",
abstract = "Mobile edge computing (MEC) is one of the promising solutions to enable augment reality (AR) by processing computational-intensive tasks within short latency constraint. However, the quality of user experience of MEC-enabled AR will significantly degrade in mobile scenarios, as it is difficult to obtain accurate channel state information (CSI) to make optimal offloading decision. In this paper, we propose an online offloading algorithm based on Lyapunov optimization to dynamically optimize the selections of the transmission rate and edge server without prior CSI. The algorithm makes a tradeoff between the reliability and latency in the MEC-enabled AR system. The numerical results show that the proposed algorithm outperforms the scheme with outdated CSI and is particularly applicable to the mobile scenarios for AR applications.",
author = "Jianhui Liu and Qi Zhang",
year = "2019",
language = "English",
isbn = "978-3-8007-4948-5",
pages = "90--95",
booktitle = "European Wireless 2019 Conference, EW 2019",
publisher = "VDE Verlag GmbH",
note = "European Wireless 2019 - 25th European Wireless Conference ; Conference date: 02-05-2019 Through 05-05-2019",
url = "https://www.vde-verlag.de/proceedings-en/564948016.html",

}

RIS

TY - GEN

T1 - Edge Computing Enabled Mobile Augmented Reality with Imperfect Channel Knowledge

AU - Liu, Jianhui

AU - Zhang, Qi

PY - 2019

Y1 - 2019

N2 - Mobile edge computing (MEC) is one of the promising solutions to enable augment reality (AR) by processing computational-intensive tasks within short latency constraint. However, the quality of user experience of MEC-enabled AR will significantly degrade in mobile scenarios, as it is difficult to obtain accurate channel state information (CSI) to make optimal offloading decision. In this paper, we propose an online offloading algorithm based on Lyapunov optimization to dynamically optimize the selections of the transmission rate and edge server without prior CSI. The algorithm makes a tradeoff between the reliability and latency in the MEC-enabled AR system. The numerical results show that the proposed algorithm outperforms the scheme with outdated CSI and is particularly applicable to the mobile scenarios for AR applications.

AB - Mobile edge computing (MEC) is one of the promising solutions to enable augment reality (AR) by processing computational-intensive tasks within short latency constraint. However, the quality of user experience of MEC-enabled AR will significantly degrade in mobile scenarios, as it is difficult to obtain accurate channel state information (CSI) to make optimal offloading decision. In this paper, we propose an online offloading algorithm based on Lyapunov optimization to dynamically optimize the selections of the transmission rate and edge server without prior CSI. The algorithm makes a tradeoff between the reliability and latency in the MEC-enabled AR system. The numerical results show that the proposed algorithm outperforms the scheme with outdated CSI and is particularly applicable to the mobile scenarios for AR applications.

M3 - Article in proceedings

SN - 978-3-8007-4948-5

SP - 90

EP - 95

BT - European Wireless 2019 Conference, EW 2019

PB - VDE Verlag GmbH

T2 - European Wireless 2019 - 25th European Wireless Conference

Y2 - 2 May 2019 through 5 May 2019

ER -