Du, J., Yu, F.R., Lu, G. et al. (3 more authors) (2020) MEC-assisted immersive VR video streaming over terahertz wireless networks: A deep reinforcement learning approach. IEEE Internet of Things, 7 (10). pp. 9517-9529. ISSN 2327-4662
Abstract
Immersive virtual reality (VR) video is becoming increasingly popular owing to its enhanced immersive experience. To enjoy ultra-high resolution immersive VR video with wireless user equipments such as head-mounted displays (HMDs), ultra-low-latency viewport rendering and data transmission are the core prerequisites, which could not be achieved without a huge bandwidth and superior processing capabilities. Besides, potentially very high energy consumption at the HMD may impede the rapid development of wireless panoramic VR video. Multi-access edge computing (MEC) has emerged as a promising technology to reduce both the task processing latency and the energy consumption for HMD, while bandwidth-rich THz communication is expected to enable ultra-high-speed wireless data transmission. In this paper, we propose to minimize the long-term energy consumption of a THz wireless access based MEC system for high quality immersive VR video services support by jointly optimizing the viewport rendering offloading and downlink transmit power control. Considering the time-varying nature of wireless channel conditions, we propose a deep reinforcement learning based approach to learn the optimal viewport rendering offloading and transmit power control policies and an asynchronous advantage actor-critic (A3C) based joint optomization algorithm is proposed. Simulation results demonstrate that the proposed algorithm converges fast under different learning rates, and outperforms existing algorithms in terms of minimized energy consumption and maximized reward.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Computation offloading; virtual reality; THz communication; deep reinforcement learning; asynchronous advantage actor-critic |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 21 Jul 2020 10:07 |
Last Modified: | 24 May 2022 10:33 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/jiot.2020.3003449 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:163497 |