  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 VR users over THz bands and light-emitting
diodes (LEDs) provide accurate indoor positioning services for them using VLC.
Here, VR users move in real time and their movement patterns change over time
according to their applications. 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 establish THz
links without body blockage to transmit the VR content. The problem is
formulated as an optimization problem whose goal is to maximize the average
number of successfully served VR users by selecting the appropriate VAPs to be
turned on and controlling the user association with SBSs. To solve this
problem, a meta policy gradient (MPG) algorithm that enables the trained policy
to quickly adapt to new user movement patterns is proposed. In order to solve
the problem for VR scenarios with a large number of users, a dual method based
MPG algorithm (D-MPG) with a low complexity is proposed. Simulation results
demonstrate that, compared to a baseline trust region policy optimization
algorithm (TRPO), the proposed MPG and D-MPG algorithms yield up to 38.2% and
33.8% improvement in the average number of successfully served users as well as
75% and 87.5% gains in the convergence speed, respectively.
