  The quality of experience (QoE) requirements of wireless Virtual Reality (VR)
can only be satisfied with high data rate, high reliability, and low VR
interaction latency. This high data rate over short transmission distances may
be achieved via abundant bandwidth in the terahertz (THz) band. However, THz
waves suffer from severe signal attenuation, which may be compensated by the
reconfigurable intelligent surface (RIS) technology with programmable
reflecting elements. Meanwhile, the low VR interaction latency may be achieved
with the mobile edge computing (MEC) network architecture due to its high
computation capability. Motivated by these considerations, in this paper, we
propose a MEC-enabled and RIS-assisted THz VR network in an indoor scenario, by
taking into account the uplink viewpoint prediction and position transmission,
MEC rendering, and downlink transmission. We propose two methods, which are
referred to as centralized online Gated Recurrent Unit (GRU) and distributed
Federated Averaging (FedAvg), to predict the viewpoints of VR users. In the
uplink, an algorithm that integrates online Long-short Term Memory (LSTM) and
Convolutional Neural Networks (CNN) is deployed to predict the locations and
the line-of-sight and non-line-of-sight statuses of the VR users over time. In
the downlink, we further develop a constrained deep reinforcement learning
algorithm to select the optimal phase shifts of the RIS under latency
constraints. Simulation results show that our proposed learning architecture
achieves near-optimal QoE as that of the genie-aided benchmark algorithm, and
about two times improvement in QoE compared to the random phase shift selection
scheme.
