混合近远场信道下可移动子阵列辅助的集成感知与通信（ISAC）系统
Movable Subarray-Aided ISAC in Hybrid Near-Far Field Channels
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中文总结 AI 辅助
该研究提出混合近远场信道模型下可移动子阵列辅助的ISAC系统，联合优化发射波束成形矩阵与子阵列位置，经数值验证可有效降低CRB。
中文摘要 AI 辅助
本研究针对可移动子阵列（MSAs）辅助的集成感知与通信（ISAC）系统，采用混合近远场信道模型展开研究。假设感知目标与通信用户处于整个MSA孔径的近场区域，但处于每个子阵列的远场区域，据此建立混合近远场信道模型，并推导联合距离、仰角与方位估计的等效费希尔信息矩阵及克拉美-罗界（CRB）。在满足最小通信信干噪比（SINR）、最大发射功率和子阵列移动约束的条件下，联合优化发射波束成形矩阵与子阵列位置，以最小化CRB的迹值。提出一种交替优化算法，结合迭代秩-1惩罚半定松弛、投影有限差分块下降及回溯方法。数值结果表明，该混合场模型与球面波模型高度吻合，且MSA可显著降低CRB。
英文摘要
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.
