Device-Centric ISAC for Exposure Control via Opportunistic Virtual Aperture Sensing
2026-02-19 • Emerging Technologies
Emerging Technologies
AI summaryⓘ
The authors developed a new way for handheld devices to measure how far they are from a user's body using the same signals they send for communication. This helps devices reduce power more precisely instead of just turning it down completely when close to the body, improving signal quality safely. They used the natural movement of the user's hand to gain detailed location info and combined it with sensors inside the device, fixing errors with a special algorithm. Their tests show that the system can measure distance very accurately, at the centimeter level, using realistic technology.
Maximum Permissible Exposure (MPE)Integrated Sensing and Communication (ISAC)uplink waveformvirtual apertureinertial sensorsextended Kalman filterphase observationBayesian Cramér-Rao boundlocalization28 GHz communication
Authors
Marouan Mizmizi, Zhibin Yu, Guanglong Du, Umberto Spagnolini
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.