Near-Field User Location Inference From Far-Field Power Measurements

TL;DR

This paper demonstrates that far-field power measurements can be used for passive near-field user localization by exploiting leakage patterns from large-aperture arrays, with theoretical bounds and deep learning methods confirming feasibility.

Contribution

It introduces a novel passive localization method leveraging leakage patterns in power measurements, supported by theoretical bounds and deep learning estimators.

Findings

Localization accuracy improves with more sensors and snapshots.

Deep learning estimators outperform grid-search in accuracy.

Reliable localization is feasible under LoS and multipath conditions.

Abstract

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.