Movable Subarray-Aided ISAC in Hybrid Near-Far Field Channels
2026-08-03 • Information Theory
Information Theory
AI summaryⓘ
The authors study a system that can both sense objects and communicate, using special antenna parts called movable subarrays. They consider a situation where targets are close enough to the whole antenna setup to need a near-field model but far enough from each subarray to use a far-field approach. They create a math model to understand how well they can estimate the target’s location and optimize the antenna signals and subarray positions to improve this while keeping communication quality. Their results show their model is accurate and that movable subarrays help make sensing more precise.
Integrated Sensing and Communication (ISAC)Movable Subarrays (MSAs)Near-field and Far-fieldHybrid Channel ModelFisher Information MatrixCramér-Rao Bound (CRB)BeamformingSignal-to-Interference-plus-Noise Ratio (SINR)Semidefinite RelaxationOptimization Algorithms
Authors
Ruiqi Liu, Yuanshuo Gang, Honghao Wang, Tianqi Mao
Abstract
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.