AI 中文总结
本文提出一种基于电磁可重构天线的学习型主动感知框架，利用LSTM积累时序特征、GNN捕捉多用户耦合，实现多用户渐进式定位，性能优于传统非可重构阵列及基线。
AI 中文摘要
本文研究由电磁可重构天线（ERA）辅助的上行无线系统中的多用户定位问题。与传统定位方案不同，我们将其建模为主动感知问题，基站（BS）利用前序感知阶段积累的历史导频观测值，调整共享ERA配置并逐步优化位置估计。为兼顾理论灵活性与实际硬件约束，我们建立统一的宽带几何信号模型，涵盖两种互补ERA范式：采用球谐基函数的合成模型，以及基于实测辐射码本的有限状态模型。由于观测维度高、多用户间共享孔径耦合导致联合设计问题解析求解极难，我们提出一种基于学习的主动感知框架。具体而言，与导频匹配的宽带观测被压缩为紧凑的用户级特征，并由长短期记忆（LSTM）模块顺序积累；这些时序特征随后经图神经网络（GNN）处理，以捕捉多用户共享孔径耦合；模型专属输出头生成连续合成系数或有限状态ERA选择，定位头则输出各阶段位置估计。特定信道分布下的数值结果表明，所提ERA辅助主动感知框架在各感知阶段实现渐进式定位优化，且性能优于传统非可重构阵列及代表性消融基线。
英文摘要
This paper investigates multi-user localization in uplink wireless systems assisted by electromagnetically reconfigurable antennas (ERAs). Unlike traditional localization schemes, we formulate an active sensing problem where a base station (BS) exploits historical pilot observations accumulated over previous sensing stages to adapt the shared ERA configuration and progressively refine position estimates. To capture both theoretical flexibility and practical hardware constraints, we establish a unified wideband geometric signal model accommodating two complementary ERA paradigms: a synthesis-based model utilizing spherical-harmonic basis functions, and a finite-state model based on measured radiation codebooks. Because analytically solving the resulting joint design problem is highly intractable due to the high-dimensional observation and the shared-aperture coupling among multiple users, we develop a learning-based active sensing framework. Specifically, pilot-matched wideband observations are compressed into compact user-wise features and sequentially accumulated by a long short-term memory (LSTM) module. These temporal features are then processed by a graph neural network (GNN) to capture multi-user shared-aperture coupling. Model-specific output heads generate either continuous synthesis coefficients or finite-state ERA selections, while a localization head produces stage-wise position estimates. Numerical results under a specific channel distribution show that the proposed ERA-assisted active sensing framework achieves progressive localization refinement across sensing stages and obtains better performance than conventional non-reconfigurable arrays and representative ablation baselines.
