发表机构
Beijing Jiaotong University（北京交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对里德伯量子阵列在低信噪比下的阈值崩溃问题，提出基于随机Barankin界最小化的物理感知功率优化框架，结合EIT读出模型和MM算法，提升太赫兹DOA估计的可靠性。
AI 中文摘要
里德伯原子量子均匀线性阵列（RAQ-ULA）为太赫兹（THz）波束对准中的到达方向（DOA）估计提供了一种有前景的传感架构。现有研究通常将量子接收器建模为宏观线性模块，并依赖克拉美-罗界（CRB）进行阵列评估或功率分配。然而，作为局部方差界，CRB无法捕捉低信噪比（SNR）区域中由空间模糊性引起的阈值崩溃。本文提出了一种基于随机Barankin界最小化的RAQ-ULA物理感知功率优化框架。我们在低SNR可积性条件下推导了闭式随机多点Barankin界（BRB）矩阵，并引入反正弦先验校准以表征有界域误差饱和。通过结合Lindblad引导的电磁感应透明（EIT）读出模型，我们将模糊敏感的BRB与光子散粒噪声、功率展宽和激光拉比频率耦合。进一步推导了CRB优化的解析基线，以揭示基于局部SNR分配的局限性。为解决由此产生的非凸问题，我们开发了一种带有Lipschitz二次代理的回溯最大化-最小化（MM）算法。该算法确保单调递减并收敛到可行的平稳点。仿真表明，所提框架比CRB更准确地预测阈值崩溃，并在严重THz衰减下扩大了可靠工作区域。
英文摘要
Rydberg-atom quantum uniform linear arrays (RAQ-ULAs) offer a promising sensing architecture for direction-of-arrival (DOA) estimation in terahertz (THz) beam alignment. Existing studies often model the quantum receiver as a macroscopic linear block and rely on the Cramer-Rao bound (CRB) for array evaluation or power allocation. However, as a local variance bound, the CRB cannot capture threshold breakdown caused by spatial ambiguities in low-signal-to-noise-ratio (SNR) regimes. This paper proposes a physics-aware power optimization framework for RAQ-ULAs based on stochastic Barankin bound minimization. We derive a closed-form stochastic multipoint Barankin bound (BRB) matrix under a low-SNR integrability condition and introduce an arcsine-prior calibration to characterize bounded-domain error saturation. By incorporating a Lindblad-guided electromagnetically induced transparency (EIT) readout model, we couple the ambiguity-sensitive BRB with photon shot noise, power broadening, and laser Rabi frequencies. A CRB-optimized analytical baseline is further derived to expose the limitation of local-SNR-based allocation. To solve the resulting nonconvex problem, we develop a backtracking majorization-minimization (MM) algorithm with a Lipschitz-based quadratic surrogate. The algorithm ensures monotonic decrease and converges to a feasible stationary point. Simulations show that the proposed framework predicts threshold breakdown more accurately than the CRB and enlarges the reliable operating region under severe THz attenuation.
Comments7 pages, 5 figures, IEEE GlobeCom 2026
