发表机构
Technische Universität Berlin; King Abdullah University of Science and Technology (KAUST); American University of Beirut (AUB)（柏林工业大学; 阿卜杜拉国王科技大学; 贝鲁特美国大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于物理特征与OPTICS聚类的无监督学习框架，仅利用接收信号功率变化即可准确区分UM-MIMO系统中的近场与远场区域，无需信道状态信息，仿真验证其与理论边界一致。
AI 中文摘要
无线通信与感知的信号处理技术在近场和远场传播机制下存在根本差异。因此，准确识别适用的传播区域对于在超大规模MIMO（UM-MIMO）系统中实现高效波束赋形和信道估计至关重要。本文提出了一种完全无监督的学习框架，仅基于接收信号测量值，在估计通信距离之前，且不依赖信道状态信息的情况下，区分近场与远场传播。所提方法利用UM阵列子阵列间的空间信号功率变化作为物理启发的特征提取阶段，随后采用OPTICS聚类算法推断通信区域。在不同系统配置和信噪比（SNR）水平下的仿真结果表明，所提方法能够准确识别近场和远场区域，与理论边界一致。
英文摘要
Signal processing techniques for wireless communications and sensing fundamentally differ between near-field and far-field propagation regimes. Accurately identifying the applicable propagation region is therefore essential for enabling efficient beamforming and channel estimation in ultra-massive MIMO (UM-MIMO) systems. This paper proposes a fully unsupervised learning framework to distinguish near-field from far-field propagation based solely on received signal measurements, before estimating the communication distance, and without relying on channel state information. The proposed approach exploits spatial signal power variations across subarrays of a UM array as a physics-inspired feature extraction stage, followed by the OPTICS clustering algorithm to infer the communication region. Simulation results under various system configurations and signal-to-noise ratio (SNR) levels demonstrate that the proposed method accurately identifies the near-field and far-field regions, showing agreement with theoretical boundaries.
