Partial Offloading MEC Optimization Scheme using Deep Reinforcement Learning for XR Real-Time MS Devices

Yunyeong Goh; Minsu Choi; Jaewook Jung; Jong Moon Chung

doi:10.1109/ICCE53296.2022.9730284

Partial Offloading MEC Optimization Scheme using Deep Reinforcement Learning for XR Real-Time MS Devices

Yunyeong Goh, Minsu Choi, Jaewook Jung, Jong Moon Chung

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

With the advent of 5G, the development of extended reality (XR) technology, which combines augmented reality (AR), virtual reality (VR), and advanced human-computer interaction (HCI) technology, is considered one of the key technologies of future metaverse engineering. Especially, XR real-time modeling and simulation (MS) devices that can be applied to various fields (e.g., emergency training simulations, etc.) have tasks with large amounts of data to be processed. However, if the XR task is processed only by wireless user equipment (UE), the UE's energy may be quickly depleted, and the quality of service (QoS) may not be satisfied. To solve these problems, this paper proposes a partial offloading optimization scheme through multiple access edge computing (MEC). In addition, deep reinforcement learning (DRL) is used to reflect the dynamic state of the MEC system and to minimize the delay. The simulation results show that the proposed scheme optimizes the delay performance by efficiently offloading the XR tasks.

Original language	English
Title of host publication	2022 IEEE International Conference on Consumer Electronics, ICCE 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665441544
DOIs	https://doi.org/10.1109/ICCE53296.2022.9730284
Publication status	Published - 2022
Event	2022 IEEE International Conference on Consumer Electronics, ICCE 2022 - Virtual, Online, United States Duration: 2022 Jan 7 → 2022 Jan 9

Publication series

Name	Digest of Technical Papers - IEEE International Conference on Consumer Electronics
Volume	2022-January
ISSN (Print)	0747-668X

Conference

Conference	2022 IEEE International Conference on Consumer Electronics, ICCE 2022
Country/Territory	United States
City	Virtual, Online
Period	22/1/7 → 22/1/9

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This research was supported by the Ministry of Science and ICT (D0318-21-1008) and the National Fire Agency (20017102) of the Republic of Korea.

Publisher Copyright:
© 2022 IEEE.

All Science Journal Classification (ASJC) codes

Industrial and Manufacturing Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/ICCE53296.2022.9730284

Cite this

Goh, Y., Choi, M., Jung, J., & Chung, J. M. (2022). Partial Offloading MEC Optimization Scheme using Deep Reinforcement Learning for XR Real-Time MS Devices. In 2022 IEEE International Conference on Consumer Electronics, ICCE 2022 (Digest of Technical Papers - IEEE International Conference on Consumer Electronics; Vol. 2022-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE53296.2022.9730284

Goh, Yunyeong ; Choi, Minsu ; Jung, Jaewook et al. / Partial Offloading MEC Optimization Scheme using Deep Reinforcement Learning for XR Real-Time MS Devices. 2022 IEEE International Conference on Consumer Electronics, ICCE 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (Digest of Technical Papers - IEEE International Conference on Consumer Electronics).

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abstract = "With the advent of 5G, the development of extended reality (XR) technology, which combines augmented reality (AR), virtual reality (VR), and advanced human-computer interaction (HCI) technology, is considered one of the key technologies of future metaverse engineering. Especially, XR real-time modeling and simulation (MS) devices that can be applied to various fields (e.g., emergency training simulations, etc.) have tasks with large amounts of data to be processed. However, if the XR task is processed only by wireless user equipment (UE), the UE's energy may be quickly depleted, and the quality of service (QoS) may not be satisfied. To solve these problems, this paper proposes a partial offloading optimization scheme through multiple access edge computing (MEC). In addition, deep reinforcement learning (DRL) is used to reflect the dynamic state of the MEC system and to minimize the delay. The simulation results show that the proposed scheme optimizes the delay performance by efficiently offloading the XR tasks.",

author = "Yunyeong Goh and Minsu Choi and Jaewook Jung and Chung, {Jong Moon}",

note = "Funding Information: ACKNOWLEDGMENT This research was supported by the Ministry of Science and ICT (D0318-21-1008) and the National Fire Agency (20017102) of the Republic of Korea. Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Consumer Electronics, ICCE 2022 ; Conference date: 07-01-2022 Through 09-01-2022",

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Goh, Y, Choi, M, Jung, J & Chung, JM 2022, Partial Offloading MEC Optimization Scheme using Deep Reinforcement Learning for XR Real-Time MS Devices. in 2022 IEEE International Conference on Consumer Electronics, ICCE 2022. Digest of Technical Papers - IEEE International Conference on Consumer Electronics, vol. 2022-January, Institute of Electrical and Electronics Engineers Inc., 2022 IEEE International Conference on Consumer Electronics, ICCE 2022, Virtual, Online, United States, 22/1/7. https://doi.org/10.1109/ICCE53296.2022.9730284

Partial Offloading MEC Optimization Scheme using Deep Reinforcement Learning for XR Real-Time MS Devices. / Goh, Yunyeong; Choi, Minsu; Jung, Jaewook et al.

2022 IEEE International Conference on Consumer Electronics, ICCE 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (Digest of Technical Papers - IEEE International Conference on Consumer Electronics; Vol. 2022-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Chung, Jong Moon

N1 - Funding Information: ACKNOWLEDGMENT This research was supported by the Ministry of Science and ICT (D0318-21-1008) and the National Fire Agency (20017102) of the Republic of Korea. Publisher Copyright: © 2022 IEEE.

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N2 - With the advent of 5G, the development of extended reality (XR) technology, which combines augmented reality (AR), virtual reality (VR), and advanced human-computer interaction (HCI) technology, is considered one of the key technologies of future metaverse engineering. Especially, XR real-time modeling and simulation (MS) devices that can be applied to various fields (e.g., emergency training simulations, etc.) have tasks with large amounts of data to be processed. However, if the XR task is processed only by wireless user equipment (UE), the UE's energy may be quickly depleted, and the quality of service (QoS) may not be satisfied. To solve these problems, this paper proposes a partial offloading optimization scheme through multiple access edge computing (MEC). In addition, deep reinforcement learning (DRL) is used to reflect the dynamic state of the MEC system and to minimize the delay. The simulation results show that the proposed scheme optimizes the delay performance by efficiently offloading the XR tasks.

AB - With the advent of 5G, the development of extended reality (XR) technology, which combines augmented reality (AR), virtual reality (VR), and advanced human-computer interaction (HCI) technology, is considered one of the key technologies of future metaverse engineering. Especially, XR real-time modeling and simulation (MS) devices that can be applied to various fields (e.g., emergency training simulations, etc.) have tasks with large amounts of data to be processed. However, if the XR task is processed only by wireless user equipment (UE), the UE's energy may be quickly depleted, and the quality of service (QoS) may not be satisfied. To solve these problems, this paper proposes a partial offloading optimization scheme through multiple access edge computing (MEC). In addition, deep reinforcement learning (DRL) is used to reflect the dynamic state of the MEC system and to minimize the delay. The simulation results show that the proposed scheme optimizes the delay performance by efficiently offloading the XR tasks.

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Goh Y, Choi M, Jung J, Chung JM. Partial Offloading MEC Optimization Scheme using Deep Reinforcement Learning for XR Real-Time MS Devices. In 2022 IEEE International Conference on Consumer Electronics, ICCE 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (Digest of Technical Papers - IEEE International Conference on Consumer Electronics). doi: 10.1109/ICCE53296.2022.9730284

Partial Offloading MEC Optimization Scheme using Deep Reinforcement Learning for XR Real-Time MS Devices

Abstract

Publication series

Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

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