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

TL;DR

An autofocus algorithm based on extended Kalman filtering that jointly tracks the trajectory and compensates residual errors using phase observations from strong scatterers is developed and results at 28GHz demonstrate centimeter-level accuracy with realistic sensor parameters.

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.

Figures (4)

• Figure 1: Device-centric ISAC for exposure control: the smartphone exploits natural hand motion to synthesize a virtual aperture, enabling distance measurement for proportional power control. The array phase center follows trajectory $\{\mathbf{q}_m\}$. The IMU provides estimates $\hat{\mathbf{q}}_m$; the error $\boldsymbol{\delta}_m = \hat{\mathbf{q}}_m - \mathbf{q}_m$ must be compensated for coherent synthesis.
• Figure 2: EKF-based autofocus: trajectory correction and imaging impact, with $\mathbf{SNR=10}$ dB.(a) Platform trajectory in the $(x,y)$ plane, comparing the true path, the IMU-derived path affected by drift/noise, and the EKF-corrected estimate. Targets are overlaid to show the scene geometry relative to the virtual aperture. (b)Oracle image obtained by \ref{['eq:bp']} using the true trajectory, representing the best achievable coherent focusing; (c) Image obtained by \ref{['eq:bp']} using the raw IMU trajectory: trajectory errors introduce range-dependent phase mismatches across slow time, causing loss of coherent integration, defocusing, and target displacement/smearing. (d) Image obtained after autofocus using the proposed EKF method: the recovered path restores inter-pulse phase consistency.
• Figure 3: Localization RMSE versus SNR together with the ideal CRB (known aperture) and the BCRB (correlated inertial uncertainty). Oracle-AF: localization using known trajectory, EKF-AF: localization using the EKF-corrected trajectory, and IMU-AF: localization using the raw IMU trajectory.
• Figure 4: Maximum allowable EIRP versus head/torso distance under FCC power-density constraint. Baseline: binary proximity-driven compliance at $r_s=2.5$ cm. Proposed: distance-aware policy using $r_{\mathrm{eff}}=\hat{r}-k\sqrt{\mathrm{CRB}(\hat{r})}$ with $k=2.58$, saturating at $\mathrm{EIRP}_{\max}$ for off-body operation ($r>50$ cm).