Device-Centric ISAC for Exposure Control via Opportunistic Virtual Aperture Sensing

2026-02-19

AI Summary

This work addresses the limitation of existing handheld devices that support only binary proximity detection when close to the human body, which often leads to excessive transmit power reduction and degraded communication quality. To overcome this, the authors propose a ranging method that constructs a virtual aperture by leveraging uplink communication waveforms and the user’s natural hand motion. Precise localization is achieved through joint trajectory tracking and phase correction. The core innovation lies in a novel extended Kalman filter–based autofocus algorithm that enables high-accuracy ranging under unknown and opportunistic device trajectories. Furthermore, the study derives a Bayesian Cramér–Rao bound that incorporates error correlations from inertial sensors. Experimental validation at 28 GHz with realistic sensor parameters demonstrates centimeter-level ranging accuracy.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Motion & Tracking
• Humans and AI: Interaction Techniques and Devices

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

Regulatory limits on Maximum Permissible Exposure (MPE) require handheld devices to reduce transmit power when operated near the user's body. Current proximity sensors provide only binary detection, triggering conservative power back-off that degrades link quality. If the device could measure its distance from the body, transmit power could be adjusted proportionally, improving throughput while maintaining compliance. This paper develops a device-centric integrated sensing and communication (ISAC) method for the device to measure this distance. The uplink communication waveform is exploited for sensing, and the natural motion of the user's hand creates a virtual aperture that provides the angular resolution necessary for localization. Virtual aperture processing requires precise knowledge of the device trajectory, which in this scenario is opportunistic and unknown. One can exploit onboard inertial sensors to estimate the device trajectory; however, the inertial sensors accuracy is not sufficient. To address this, we develop an autofocus algorithm based on extended Kalman filtering that jointly tracks the trajectory and compensates residual errors using phase observations from strong scatterers. The Bayesian Cramér-Rao bound for localization is derived under correlated inertial errors. Numerical results at 28GHz demonstrate centimeter-level accuracy with realistic sensor parameters.

Problem

• Maximum Permissible Exposure
• proximity sensing
• transmit power control
• device-centric ISAC
• distance estimation

Innovation

• Integrated Sensing and Communication (ISAC)
• Virtual Aperture
• Autofocus Algorithm
• Extended Kalman Filter
• Maximum Permissible Exposure (MPE)