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 Cram\'er-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.
@article{arxiv.2605.21815,
title = {Near-Field User Location Inference From Far-Field Power Measurements},
author = {Shima Mashhadi and Tiep M. Hoang and Alireza Vahid},
journal= {arXiv preprint arXiv:2605.21815},
year = {2026}
}
Comments
To appear in IEEE Vehicular Technology Conference: VTC2026-Fall