Beamforming guided by task improves wireless location accuracy near boundaries
Task-Aware Beamforming for Semantic Wireless Localization
Information Theory
Summary
Finding the exact position of something using wireless signals isn’t always enough, especially when the goal is to know which area or region it belongs to. This paper shows how to focus the signal receiver’s attention on the important boundary between regions to improve these kinds of decisions. The authors create a method that allocates the receiver’s resources so it gets the most useful information about location near these boundaries, reducing mistakes in region identification. Their tests show this method works better than standard approaches that only aim to minimize general position error.
What this means in practice
- •For wireless network engineers: Optimize uplink beamforming to improve location-based decisions like geofencing and intrusion detection near region boundaries.
- •For industrial automation teams: Enhance wireless localization for task-specific state detection within mapped facility zones, improving operational safety and tracking.
Authors
Mohammad Mahdi Mojahedian, Alireza Pourafzal, Musa Furkan Keskin, Henk Wymeersch
Abstract
Many semantic localization tasks require determining a contextually meaningful region or state rather than minimizing the error of a full Cartesian position estimate. In such settings, aggregate localization accuracy need not align with the accuracy of the semantic decision. This paper develops a Cramér--Rao bound (CRB)-based framework that uses the local geometry of a known semantic map to guide uplink receive beamforming. Using local boundary crossing as a surrogate for semantic misclassification, we derive normal information maximization (NIM), which minimizes the CRB of the position component along the local semantic boundary normal. Under the considered single-path line-of-sight model, an optimal receive codebook can be restricted to the subspace spanned by the matched steering vector and its angular derivative, reducing the design to an allocation of measurement resources between matched and derivative spatial modes. We derive the resulting allocation for a general smooth boundary, with geofencing and intrusion detection arising as radial and tangential limiting cases. The framework is further extended to multi-region semantic maps through a distance-normalized minimax criterion and to uncertain prior locations through worst-case and prior-weighted robust formulations. Numerical results with finite-slot implementations and observation-level Monte Carlo simulations using profile maximum-likelihood (ML) estimation show that NIM allocates measurements according to the task-relevant boundary geometry and can substantially reduce semantic error relative to the classic squared position error bound (SPEB) design in the considered scenarios. The empirical results also closely follow the local CRB-based boundary-crossing approximation in the studied operating regime.