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.
The exposure of the human body to radiofrequency electromagnetic waves is regulated, in near field, through limits on the maximum permissible exposure (MPE). Regulatory frameworks accordingly prescribe maximum admissible levels, and portable devices are required to demonstrate compliance under worst-case operating conditions, notably when the handset is used in close proximity to the head during voice services [1], [2].
Current devices employ proximity sensors to detect the presence of a conductive body to trigger transmit power backoff when the separation falls below approximately 25 mm [3]. This mechanism is essentially binary: it provides no estimate of the separation distance and thus enforces conservative operation. The throughput degradation becomes acute in fifthgeneration (5G) networks at millimeter-wave (mmWave) frequencies, where the link budget is already constrained by propagation losses [4]. If the smartphone could measure its distance from the head, proportional power control would become feasible.
Integrated Sensing and Communication (ISAC) has emerged as a transformative paradigm for sixth-generation (6G) net-works, enabling simultaneous wireless communication and sensing through shared waveforms, spectrum, and hardware [5], [6]. Prior work has addressed network-centric localization from base-station infrastructure [7]- [10], bistatic vehicular configurations [11], [12], and virtual aperture techniques with motion compensation [13]- [16]. Device-centric ISAC, where a handheld terminal performs monostatic sensing using its own motion, remains largely unexplored.
This paper proposes a device-centric ISAC framework for smartphone MPE compliance based on opportunistic virtual aperture formation. The device exploits its motion during normal operation to synthesize a virtual sensing aperture, enabling accurate localization of the user’s body for distanceproportional power control. The main contributions are as follows. First, we formulate the near-field monostatic sensing problem at mmWave frequencies, modeling the signal, antenna configuration, and inertial measurement unit (IMU) based positioning of the device, and compensating for the errors. Second, we derive the Bayesian Cramér-Rao Bound for target localization that incorporates aperture position uncertainty through a correlated Gaussian prior, revealing the fundamental accuracy limits imposed by IMU errors. Third, we develop a complete sensing algorithm comprising relative positionbased, phase autofocusing, and backprojection imaging, with explicit power control mapping for MPE compliance. Numerical simulation results demonstrate that centimeter-level localization accuracy is achievable at 28 GHz with realistic motion trajectories and sensor specifications.
The remainder of this paper is organized as follows. Section II introduces the system model, including the monostatic signal model and the IMU-based trajectory error characterization. Section III derives the Bayesian Cramér-Rao bound for target localization under correlated aperture uncertainty. Section IV develops the EKF-based autofocus algorithm. Section V presents numerical results validating the bounds and quantifying the EIRP gain. Section VI concludes the paper.
Fig. 1 illustrates the device-centric sensing geometry considered in this work. A handheld device operates in close proximity to the user’s body and exploits its natural motion to perform monostatic sensing while transmitting uplink communication signals. The sensing task is to localize a small set of dominant reflection points on the body using the communication waveform itself.
The device-body distance exceeds the Fraunhofer distance of the physical antenna array, so that far-field power-density scaling proportional to r -2 applies for MPE compliance. At the same time, the same distance lies within the Fresnel region of the motion-synthesized aperture, requiring a near-field formulation with spherical wavefronts for coherent focusing.
Let q m ∈ R 3 denote the position of the array phase center at observation index m ∈ {0, 1, . . . , M -1}. The sensing environment is modeled by Q point scatterers at locations p q ∈ R 3 with complex reflectivities σ q , representing distinct reflection points on the user’s body. The position of antenna element n at time m is p n,m = q m + d n ,, where d n is the displacement of element n from the array phase center. The corresponding bistatic range is
with round-trip delay τ n,q,m = 2r n,q,m /c. The sequence {q m } defining the virtual aperture is not known exactly. The device relies on an onboard IMU to estimate the phase-center trajectory, yielding position estimates {q m }. These estimates are affected by residual errors, q m = qmδ m , where δ m denotes the unknown position error at observation m.
The transmitted signal is an OFDM waveform with K subcarriers spanning bandwidth B and spacing ∆f = B/K. The k-th subcarrier occupies frequency f k = f c + k∆f for k = 0, . . . , K -1.
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