  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.
