Near-Field User Location Inference From Far-Field Power Measurements
For 6G systems with extremely large-aperture arrays, this enables passive localization without active user participation, though the method is incremental as it applies known inference techniques to a new signal model.
The paper shows that passive user localization is feasible by exploiting structured leakage from near-field beamfocusing, using only far-field power measurements. Results confirm reliable inference with accuracy improving with more sensors and snapshots.
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.