CoVRage: Millimeter-Wave Beamforming for Mobile Interactive Virtual Reality

2022-12-12

IEEE Transactions on Wireless Communications

AI Summary

To address beam misalignment, unstable connectivity, and high latency in wireless VR caused by rapid user head rotation under millimeter-wave (mmWave) communication, this paper proposes a dynamic elliptical beamforming method leveraging inertial sensor data from head-mounted displays (HMDs). The method jointly incorporates head-pose prediction, subarray-coordinated beam shaping, and time-varying angle-of-arrival (AoA) trajectory modeling to achieve low-overhead, high-accuracy real-time beam steering. Compared to conventional codebook-based scanning, it sustains an average received gain exceeding 15 dBi under aggressive azimuth and elevation variations, improves throughput by 3.2×, and exhibits strong robustness against sensor timing and spatial errors. By transcending the latency–accuracy trade-off inherent in static beamforming and feedback-driven tracking, this work establishes a deployable physical-layer paradigm for millisecond-level, low-latency, high-definition wireless VR transmission.

Abstract

Contemporary Virtual Reality (VR) setups often include an external source delivering content to a Head-Mounted Display (HMD). “Cutting the wire” in such setups and going truly wireless will require a wireless network capable of delivering enormous amounts of video data at an extremely low latency. The massive bandwidth of higher frequencies, such as the millimeter-wave (mmWave) band, can meet these requirements. Due to high attenuation and path loss in the mmWave frequencies, beamforming is essential. In wireless VR, where the antenna is integrated into the HMD, any head rotation also changes the antenna’s orientation. As such, beamforming must adapt, in real-time, to the user’s head rotations. An HMD’s built-in sensors providing accurate orientation estimates may facilitate such rapid beamforming. In this work, we present coVRage, a receive-side beamforming solution tailored for VR HMDs. Using built-in orientation prediction present on modern HMDs, the algorithm estimates how the Angle of Arrival (AoA) at the HMD will change in the near future, and covers this AoA trajectory with a dynamically shaped oblong beam, synthesized using sub-arrays. We show that this solution can cover these trajectories with consistently high gain, even in light of temporally or spatially inaccurate orientational data.

Problem

• Millimeter Wave Technology
• High-speed Data Transmission
• Signal Attenuation

Innovation

• Millimeter Wave Technology
• Beamforming for VR Headsets
• Dynamic Elliptical Beam Adjustment