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.

翻译：虚拟现实（VR）技术的发展重心在于提升沉浸感、支持多用户虚拟体验（VE），并通过重定向行走（RDW）技术使佩戴者能在专用VR空间中自由移动。为满足其极高的数据速率和延迟要求，未来VR系统将需要运行在毫米波（mmWave）频段的无线网络基础设施支持，该频段通过波束赋形和波束转向技术实现收发端的高度定向通信。本文提出利用预测性上下文感知来优化收发端的波束赋形与波束转向。通过预测多用户VR重定向行走（RDW）场景下用户的短期横向移动，可在用户方向实现视距（LoS）“追踪”，从而优化发射端波束赋形与波束转向。同时，短期朝向运动预测可用于优化接收端波束赋形，提升覆盖灵活性。我们针对这两类上下文信息预测中的两个开放性问题展开研究：i) 预测多用户VR重定向行走（RDW）场景下的横向移动，ii) 生成用于训练朝向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）网络在横向移动预测中具有显著的预测精度，且虚拟环境（VE）带来的上下文感知特性可进一步提升该精度。此外，我们证明基于TimeGAN的朝向数据生成方法能够创建与实验数据高度一致的合成样本。