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Pose-aware 3D Beamwidth Adaptation for Mobile Extended Reality

Alperen Duru, Mohammad Mozaffari, Mehrnaz Afshang, Ticao Zhang, Talha Khan, Todd E. Humphreys, Jeffrey G. Andrews

TL;DR

This work addresses beam misalignment in pose-rich XR scenarios by introducing a sensor-aided, pose-aware beamwidth adaptation method that operates on the user device using a 2D antenna array. By leveraging the DoA estimation covariance to form an elliptical confidence interval, the beam is aligned with the uncertainty region, and a subset of antennas is activated to match the ellipse, yielding improved outage performance and power efficiency. The approach demonstrates up to an 8% increase in coverage distance and over 15% growth in coverage area, with up to 18% energy savings, highlighting practical gains for outdoor XR at mmWave frequencies. The method generalizes to 6DoF terminals and provides a framework for extending both coverage and battery life in XR systems through covariance-informed beam shaping.

Abstract

This paper presents a sensor-aided pose-aware beamwidth adaptation design for a conceptual extended reality (XR) Head-Mounted Display (HMD) equipped with a 2D planar array. The beam is tracked and adapted on the user side by leveraging HMD orientation estimates. The beamwidth adaptation scheme is effected by selective deactivation of elements in the 2D antenna array, employing the angular estimation covariance matrix to overlap the beam with the estimation confidence interval. The proposed method utilizes the estimation correlations to adapt the beamwidth along the confidence interval of these estimates. Compared to a beamwidth adaptation without leveraging estimation correlations, the proposed method demonstrates the gain of leveraging estimation correlations by improving the coverage area for a given outage probability threshold by approximately 16%, or equivalently increasing the power efficiency up to 18%.

Pose-aware 3D Beamwidth Adaptation for Mobile Extended Reality

TL;DR

This work addresses beam misalignment in pose-rich XR scenarios by introducing a sensor-aided, pose-aware beamwidth adaptation method that operates on the user device using a 2D antenna array. By leveraging the DoA estimation covariance to form an elliptical confidence interval, the beam is aligned with the uncertainty region, and a subset of antennas is activated to match the ellipse, yielding improved outage performance and power efficiency. The approach demonstrates up to an 8% increase in coverage distance and over 15% growth in coverage area, with up to 18% energy savings, highlighting practical gains for outdoor XR at mmWave frequencies. The method generalizes to 6DoF terminals and provides a framework for extending both coverage and battery life in XR systems through covariance-informed beam shaping.

Abstract

This paper presents a sensor-aided pose-aware beamwidth adaptation design for a conceptual extended reality (XR) Head-Mounted Display (HMD) equipped with a 2D planar array. The beam is tracked and adapted on the user side by leveraging HMD orientation estimates. The beamwidth adaptation scheme is effected by selective deactivation of elements in the 2D antenna array, employing the angular estimation covariance matrix to overlap the beam with the estimation confidence interval. The proposed method utilizes the estimation correlations to adapt the beamwidth along the confidence interval of these estimates. Compared to a beamwidth adaptation without leveraging estimation correlations, the proposed method demonstrates the gain of leveraging estimation correlations by improving the coverage area for a given outage probability threshold by approximately 16%, or equivalently increasing the power efficiency up to 18%.
Paper Structure (15 sections, 15 equations, 7 figures, 1 table)

This paper contains 15 sections, 15 equations, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work and Its Limitations
  3. Contributions
  4. System Model
  5. Outage Probability
  6. Beamwidth Adaptation Approach
  7. General Setup and Problem Formulation
  8. Confidence Interval Estimation
  9. Outage Probability Optimization
  10. Generalized Beamwidth Adaptation
  11. Results and Discussion
  12. Optimal Number of Active Antennas
  13. Changing Correlation’s Effect on Outage Probability
  14. Confidence Interval Orientation's Effect on Outage Probability
  15. Conclusions

Figures (7)

  • Figure 1: Conceptual XR design with onboard GNSS and IMU sensors.
  • Figure 2: Beam perfectly overlapping with the confidence interval (left), beam eccentricity is the same as the confidence interval, but orientation is not aligned (middle), beam eccentricity is not the same as the confidence interval (right).
  • Figure 3: Transformation of confidence interval (left) to the resultant beam shape (right).
  • Figure 4: Number of allowable antennas versus distance for the 3GPP antenna model.
  • Figure 5: Optimal number of antennas versus distance for the 3GPP antenna model.
  • ...and 2 more figures