Meta-Reinforcement Learning for Immersive Virtual Reality over THz/VLC Wireless Networks

Yining Wang, Mingzhe Chen, Zhaohui Yang, Walid Saad, Tao Luo, Shuguang Cui, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


In this paper, the problem of enhancing the quality of virtual reality (VR) services is studied for an indoor terahertz (THz)/visible light communication (VLC) wireless network. In the studied model, small base stations (SBSs) transmit high-quality VR images to users over THz bands and light-emitting diodes (LEDs) provide accurate indoor positioning services for VR users using VLC. Here, VR users move in real time and their movement patterns change over time according to their application. Both THz and VLC links can be blocked by the bodies of VR users. To control the energy consumption of the studied THz/VLC wireless VR network, VLC access points (VAPs) must be selectively turned on so as to ensure accurate and extensive positioning for VR users. Based on the user positions, each SBS must generate corresponding VR images and build THz links without body blockage to transmit the VR content. The problem is formulated as an optimization problem whose goal is to maximize the sum successful transmission probability of all VR users by selecting the appropriate VAPs to be turned on and controlling the user association with SBSs. To solve this problem, a policy gradient-based reinforcement learning (RL) algorithm using meta-learning framework is proposed. The proposed algorithm can effectively solve the formulated problem and enable the trained policy to quickly adapt to new user movement patterns. Simulation results demonstrate that, compared to a baseline trust region policy optimization algorithm (TRPO), the proposed meta-learning solution yields a 78% improvement in the convergence speed and about 16.4% improvement in the sum successful transmission probabilities of all VR users.

Original languageEnglish (US)
Title of host publicationICC 2021 - IEEE International Conference on Communications, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728171227
StatePublished - Jun 2021
Externally publishedYes
Event2021 IEEE International Conference on Communications, ICC 2021 - Virtual, Online, Canada
Duration: Jun 14 2021Jun 23 2021

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607


Conference2021 IEEE International Conference on Communications, ICC 2021
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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