期刊
IEEE INTERNET OF THINGS JOURNAL
卷 7, 期 10, 页码 9517-9529出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2020.3003449
关键词
Wireless communication; Task analysis; Rendering (computer graphics); Streaming media; Optimization; Resists; Resource management; Asynchronous advantage actor-critic (A3C); computation offloading; deep reinforcement learning (DRL); terahertz (THz) communication; virtual reality (VR)
资金
- Natural Science Foundation of Shaanxi Province [2020JQ-844]
- Natural Science Foundation of China [61901367, 61871321, 61901381, 61901370]
- Science and Technology Innovation Team of Shaanxi Province for Broadband Wireless and Application [2017KCT-30-02]
- National Science Foundation of Shaanxi Province [2019JM-442]
- National Key Research and Development Program of China [2018YFE0126000]
- Key Research and Development Plan of Shaanxi Province [2017ZDCXL-GY-05-01]
- Xi'an Key Laboratory of Mobile Edge Computing and Security [201805052-ZD3CG36]
Immersive virtual reality (VR) video is becoming increasingly popular owing to its enhanced immersive experience. To enjoy ultrahigh resolution immersive VR video with wireless user equipments, such as head-mounted displays (HMDs), ultralow-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. Multiaccess 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 terahertz (THz) communication is expected to enable ultrahigh-speed wireless data transmission. In this article, 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 optimization algorithm is proposed. The 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.
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