Movable Subarray-Aided ISAC in Hybrid Near-Far Field Channels

2026-08-03

AI Summary

This work addresses the challenge of channel modeling and performance optimization in integrated sensing and communication (ISAC) systems operating in a hybrid near–far-field regime, where the overall array resides in the near field while individual subarrays experience far-field conditions. To tackle this, the paper proposes a novel architecture based on movable subarrays (MSA). A hybrid near–far-field channel model is developed, and transmit beamforming along with subarray positions are jointly optimized to minimize the Cramér–Rao bound (CRB) for angle and distance estimation, subject to constraints on communication SINR, transmit power, and subarray mobility. The main contributions include the first introduction of MSA for hybrid-field scenarios, derivation of the equivalent Fisher information matrix and CRB, and formulation of a joint optimization framework balancing sensing accuracy and communication quality. An efficient alternating optimization algorithm—combining iterative rank-one penalty semidefinite relaxation, projected finite-difference block coordinate descent, and backtracking—is designed to solve the resulting non-convex problem. Simulations confirm the proposed model’s close agreement with spherical wave models and demonstrate that MSA significantly reduces CRB, thereby enhancing sensing performance.

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.

Problem

• Integrated Sensing and Communication
• Movable Subarrays
• Hybrid Near-Far Field
• Cramér-Rao Bound
• Joint Parameter Estimation

Innovation

• movable subarrays
• hybrid near-far field
• ISAC
• Cramér-Rao bound
• alternating optimization