Task-Aware Beamforming for Semantic Wireless Localization
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.
Related
- Universal CKM for Environment-Aware Wireless Networks: Enabling Cross-Device and Cross-Task Channel Knowledge Transfer
- Bayesian-Guided Cooperative RL Beamforming for Wireless Adversarial User Detection
- The Interference-Conjugate Transform: The Tightest Interference-Linear Surrogate for Power Control, Scheduling and Beamforming in Wireless Networks
- Joint Flexible Beamforming, Resource Allocation, and Task Buffering for Movable-Antenna Enabled MEC Systems
- GSBF: Gaussian Splatting for Environment-Aware Beamforming
BibTeX
@misc{mojahedian2026taskaware,
title = {{Task-Aware Beamforming for Semantic Wireless Localization}},
author = {Mohammad Mahdi Mojahedian and Alireza Pourafzal and Musa Furkan Keskin and Henk Wymeersch},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.14801},
eprint = {2609.14801},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.14801},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Task-Aware Beamforming for Semantic Wireless Localization
AU - Mohammad Mahdi Mojahedian
AU - Alireza Pourafzal
AU - Musa Furkan Keskin
AU - Henk Wymeersch
PY - 2026
DA - 2026/09/13
PB - arXiv
DO - 10.48550/arxiv.2609.14801
UR - https://arxiv.org/abs/2609.14801
AB - 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.
ER -