Movable Subarray-Aided ISAC in Hybrid Near-Far Field Channels
For ISAC system designers, this work provides a more accurate channel model and demonstrates that movable subarrays can significantly improve sensing accuracy, but the gains are specific to this setup and incremental over existing ISAC optimization.
This paper studies an ISAC system with movable subarrays in hybrid near-far field channels, deriving the CRB for sensing and jointly optimizing beamforming and subarray positions to minimize CRB under communication and power constraints. Numerical results show that the hybrid-field model closely matches the spherical-wave model and that movable subarrays substantially reduce the CRB.
This letter investigates an integrated sensing and communication (ISAC) system aided by movable subarrays (MSAs) using a hybrid near-far field channel model. The sensing target and communication users are assumed to lie in the near field of the overall MSA aperture but in the far-field region of each subarray. Accordingly, a hybrid near-far field channel model is established, and the equivalent Fisher information matrix and Cramér-Rao bound (CRB) for joint range, elevation, and azimuth estimation are derived. The transmit beamforming matrix and subarray positions are jointly optimized to minimize the trace CRB subject to minimum communication signal-to-interference-plus-noise ratio (SINR), maximum transmit power and subarray movement constraints. An alternating optimization algorithm is developed combining iterative rank-one-penalized semidefinite relaxation with projected finite-difference block descent and backtracking. Numerical results show that the hybrid-field model closely matches the spherical-wave model, while MSAs substantially reduce the CRB.