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

2026-05-20

AI Summary

This work addresses the problem of passive localization of near-field users using only far-field received power measurements. Exploiting the position-dependent power leakage characteristics induced by beam focusing with extremely large aperture arrays in the near field, the study proposes a novel passive localization mechanism based on far-field power signatures and derives the corresponding Bayesian Cramér–Rao lower bound (BCRLB). The approach integrates non-central chi-squared statistical modeling, a grid-search estimator, and an attention-based permutation-invariant deep learning regressor (DeepSet). Experimental results demonstrate that the proposed method achieves effective localization accuracy under both line-of-sight and multipath conditions, with performance significantly improving as the number of sensors and snapshots increases.

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 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.

Problem

• near-field localization
• far-field power measurements
• user location inference
• extremely large-aperture arrays
• passive sensing

Innovation

• near-field localization
• extremely large-aperture arrays
• power-based sensing
• Bayesian Cramér-Rao lower bound
• DeepSet