论文标题
无线虚拟现实（VR）网络基于学习的预测和上行链路重新启动
Learning-based Prediction and Uplink Retransmission for Wireless Virtual Reality (VR) Network
论文作者
论文摘要
无线虚拟现实（VR）用户可以随时随地享受身临其境的体验。但是，在有限的VR交互潜伏期下，提供具有高质量的完整球形VR视频是具有挑战性的。如果可以预先预测VR用户的观点，则仅需要渲染和交付所需的观点，这可以减少VR相互作用延迟。因此，在本文中，我们使用离线和在线学习算法使用REAL VR数据集预测VR用户的观点。对于离线学习算法，训练有素的学习模型直接用于预测连续时间插槽中VR用户的观点。对于在线学习算法，根据VR用户通过上行链路传输提供的实际观点，我们将其与预测的观点进行比较，并更新在线学习算法的参数，以进一步提高预测准确性。为了确保上行链路传输的可靠性，我们将主动的重新传播方案集成到我们建议的在线学习算法中。仿真结果表明，我们提出的针对上行无线VR网络的在线学习算法具有主动重传方案，仅显示约5％的预测错误。
Wireless Virtual Reality (VR) users are able to enjoy immersive experience from anywhere at anytime. However, providing full spherical VR video with high quality under limited VR interaction latency is challenging. If the viewpoint of the VR user can be predicted in advance, only the required viewpoint is needed to be rendered and delivered, which can reduce the VR interaction latency. Therefore, in this paper, we use offline and online learning algorithms to predict viewpoint of the VR user using real VR dataset. For the offline learning algorithm, the trained learning model is directly used to predict the viewpoint of VR users in continuous time slots. While for the online learning algorithm, based on the VR user's actual viewpoint delivered through uplink transmission, we compare it with the predicted viewpoint and update the parameters of the online learning algorithm to further improve the prediction accuracy. To guarantee the reliability of the uplink transmission, we integrate the Proactive retransmission scheme into our proposed online learning algorithm. Simulation results show that our proposed online learning algorithm for uplink wireless VR network with the proactive retransmission scheme only exhibits about 5% prediction error.
