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

Learning-based Prediction, Rendering and Transmission for Interactive Virtual Reality in RIS-Assisted Terahertz Networks