The advancement of Virtual Reality (VR) technology is focused on improving its immersiveness, supporting multiuser Virtual Experiences (VEs), and enabling the users to move freely within their VEs while still being confined within 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.

翻译：摘要：虚拟现实（VR）技术的发展致力于提升其沉浸感，支持多用户虚拟体验（VE），并允许用户在专用VR设备内通过重定向行走（RDW）实现自由移动，尽管实际空间受到限制。为满足其极端的数据速率和延迟要求，未来VR系统需依赖工作在毫米波（mmWave）频段的无线网络基础设施，并通过波束赋形与波束转向技术实现高定向的收发通信。我们提出利用预测性上下文感知来优化发射端与接收端的波束赋形和波束转向。通过预测多用户VR设置中结合重定向行走（RDW）的用户短期侧向运动，可优化发射端波束赋形与波束转向，实现对用户方向的视距（LoS）“追踪”。同时，短期方位运动的预测可用于增强接收端波束赋形，提升覆盖灵活性。我们针对这两个上下文信息实例的预测问题展开研究：i） 在存在RDW的多用户VR场景中预测侧向运动；ii） 生成用于训练方位运动预测器的合成头部旋转数据集。实验结果表明，长短期记忆（LSTM）网络在预测侧向运动方面展现出有前景的精度，且源自虚拟环境（VE）的上下文感知能力进一步提升了该精度。此外，我们证明一种基于TimeGAN的方位数据生成方法能够创建与实验获取样本高度匹配的合成样本。