Near-field beamfocusing enabled by extremely large-aperture arrays (ELAA) is a promising 6G technique for massive connectivity and high spectrum efficiency. While beamfocusing concentrates energy at an intended user, the radiated field outside the focal point exhibits a structured leakage that varies with the focal-point coordinates. This paper shows that this leakage enables a new form of passive user localization in which distributed far-field sensors measuring only received power can infer the user's location by exploiting this location-dependent power signature. Using the induced noncentral chi-square statistics, we derive a Bayesian Cramér-Rao lower bound (BCRLB) that establishes the fundamental limits of this inference problem. We then evaluate a model-based grid-search estimator and an attention-based permutation-invariant deep learning regressor (DeepSet). Results under both line-of-sight (LoS) and multipath propagation confirm that reliable location inference is feasible, with accuracy improving as more sensors and snapshots are used.

翻译：利用超大规模阵列（ELAA）实现的近场波束聚焦是第六代移动通信（6G）中实现大规模连接与高频谱效率的关键技术。波束聚焦将能量集中于目标用户，但焦点之外的辐射场会呈现随焦点坐标变化的结构性泄漏。研究表明，这种泄漏可实现一种新型被动用户定位：分布式远场传感器仅通过测量接收功率，即可利用与位置相关的功率特征推断用户位置。基于诱导的非中心卡方统计量，我们推导了贝叶斯克拉默-拉奥下界（BCRLB），确立了该推断问题的理论极限。进一步，我们评估了基于模型的网格搜索估计器与基于注意力机制的置换不变深度学习回归器（DeepSet）。视距（LoS）与非视距多径传播条件下的实验结果表明，可靠的位置推断是可行的，且精度随传感器数量与快照数量的增加而提升。