User Localization via Active Sensing with Electromagnetically Reconfigurable Antennas

TL;DR

This paper introduces a deep learning-based active sensing framework utilizing electromagnetically reconfigurable antennas to improve user localization accuracy through diversified measurements and sequential refinement.

Contribution

It proposes a novel end-to-end system combining ERA reconfigurability, attention-based feature extraction, and LSTM learning for enhanced localization performance.

Findings

Outperforms traditional digital beamforming in accuracy

Effectively refines user position estimates over time

Demonstrates the benefits of ERA-enabled active sensing

Abstract

This paper presents an end-to-end deep learning framework for electromagnetically reconfigurable antenna (ERA)-aided user localization with active sensing, where ERAs provide additional electromagnetic reconfigurability to diversify the received measurements and enhance localization informativeness. To balance sensing flexibility and overhead, we adopt a two-timescale design: the digital combiner is updated at each stage, while the ERA patterns are reconfigured at each substage via a spherical-harmonic representation. The proposed mechanism integrates attention-based feature extraction and LSTM-based temporal learning, enabling the system to learn an optimized sensing strategy and progressively refine the UE position estimate from sequential observations. Simulation results show that the proposed approach consistently outperforms conventional digital beamforming-only and single-stage sensing baselines in terms of localization accuracy. These results highlight the effectiveness of ERA-enabled active sensing for user localization in future wireless systems.