面向全沉浸式多用户虚拟现实与重定向行走的预测性上下文感知
Predictive Context-Awareness for Full-Immersive Multiuser Virtual Reality with Redirected Walking
AI总结：
本文提出利用预测性上下文感知优化毫米波VR系统的波束成形与波束转向，通过LSTM预测多用户重定向行走中的横向移动，并用TimeGAN生成头部旋转数据，提升通信性能。
摘要：
虚拟现实（VR）技术的进步聚焦于提升其沉浸感、支持多用户虚拟体验（VEs），并让用户能够在专门的VR装置范围内自由移动，同时通过重定向行走（RDW）保持身体不超出该装置范围。为了满足其极高的数据速率和时延要求，未来的VR系统将需要支持在毫米波（mmWave）频段运行的无线网络基础设施，这些基础设施通过波束成形和波束转向在发射和接收两端利用高度定向通信。我们提出使用预测性上下文感知来优化发射端和接收端的波束成形与波束转向。通过预测采用重定向行走（RDW）的多用户VR装置中用户的短期横向移动，发射端波束成形和波束转向可以通过在用户方向上的视距（LoS）“跟踪”得到优化。同时，短期朝向移动的预测可用于接收端波束成形，以增强覆盖灵活性。我们针对预测这两类上下文信息中的两个开放问题：i）在采用RDW的多用户VR环境中预测横向移动，以及ii）生成用于训练朝向移动预测器的合成头部旋转数据集。我们的实验结果表明，长短期记忆（LSTM）网络在预测横向移动方面具有可观的准确率，而源自虚拟体验（VEs）的上下文感知进一步提升了这一准确率。此外，我们展示了基于TimeGAN的朝向数据生成方法能够创建与实验获取样本高度吻合的合成样本。
英文摘要：
The advancement of Virtual Reality (VR) technology is focused on improving its immersiveness, supporting multiuser Virtual Experiences (VEs), and enabling users to move freely within their VEs while remaining confined to specialized VR setups through Redirected Walking (RDW). To meet their extreme data-rate and latency requirements, future VR systems will require supporting wireless networking infrastructures operating in millimeter Wave (mmWave) frequencies that leverage highly directional communication in both transmission and reception through beamforming and beamsteering. We propose the use of predictive context-awareness to optimize transmitter and receiver-side beamforming and beamsteering. By predicting users' short-term lateral movements in multiuser VR setups with Redirected Walking (RDW), transmitter-side beamforming and beamsteering can be optimized through Line-of-Sight (LoS) "tracking" in the users' directions. At the same time, predictions of short-term orientational movements can be utilized for receiver-side beamforming for coverage flexibility enhancements. We target two open problems in predicting these two context information instances: i) predicting lateral movements in multiuser VR settings with RDW, and ii) generating synthetic head rotation datasets for training orientational movements predictors. Our experimental results demonstrate that Long Short-Term Memory (LSTM) networks feature promising accuracy in predicting lateral movements, and context-awareness stemming from VEs further enhances this accuracy. Additionally, we show that a TimeGAN-based approach for orientational data generation can create synthetic samples that closely match experimentally obtained ones.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
