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The advancement of Virtual Reality (VR) technology is focused on improvingits immersiveness, supporting multiuser Virtual Experiences (VEs), and enablingusers to move freely within their VEs while remaining confined to specializedVR setups through Redirected Walking (RDW). To meet their extreme data-rate andlatency requirements, future VR systems will require supporting wirelessnetworking infrastructures operating in millimeter Wave (mmWave) frequenciesthat leverage highly directional communication in both transmission andreception through beamforming and beamsteering. We propose the use ofpredictive context-awareness to optimize transmitter and receiver-sidebeamforming and beamsteering. By predicting users' short-term lateral movementsin multiuser VR setups with Redirected Walking (RDW), transmitter-sidebeamforming and beamsteering can be optimized through Line-of-Sight (LoS)"tracking" in the users' directions. At the same time, predictions ofshort-term orientational movements can be utilized for receiver-sidebeamforming for coverage flexibility enhancements. We target two open problemsin predicting these two context information instances: i) predicting lateralmovements in multiuser VR settings with RDW, and ii) generating synthetic headrotation datasets for training orientational movements predictors. Ourexperimental results demonstrate that Long Short-Term Memory (LSTM) networksfeature promising accuracy in predicting lateral movements, andcontext-awareness stemming from VEs further enhances this accuracy.Additionally, we show that a TimeGAN-based approach for orientational datageneration can create synthetic samples that closely match experimentallyobtained ones.

Predictive Context-Awareness for Full-Immersive Multiuser Virtual Reality with Redirected Walking