This paper addresses an uplink localization problem in which the base station (BS) aims to locate a remote user with the aid of reconfigurable intelligent surface (RIS). This paper proposes a strategy in which the user transmits pilots over multiple time frames, and the BS adaptively adjusts the RIS reflection coefficients based on the observations already received so far in order to produce an accurate estimate of the user location at the end. This is a challenging active sensing problem for which finding an optimal solution involves a search through a complicated functional space whose dimension increases with the number of measurements. In this paper, we show that the long short-term memory (LSTM) network can be used to exploit the latent temporal correlation between measurements to automatically construct scalable information vectors (called hidden state) based on the measurements. Subsequently, the state vector can be mapped to the RIS configuration for the next time frame in a codebook-free fashion via a deep neural network (DNN). After all the measurements have been received, a final DNN can be used to map the LSTM cell state to the estimated user equipment (UE) position. Numerical result shows that the proposed active RIS design results in lower localization error as compared to existing active and nonactive methods. The proposed solution produces interpretable results and is generalizable to early stopping in the sequence of sensing stages.

翻译：本文研究了上行链路定位问题，其中基站（BS）借助可重构智能表面（RIS）实现对远端用户的位置估计。本文提出一种策略：用户在多时间帧内发送导频，基站根据已接收到的观测结果自适应调整RIS反射系数，以在最终阶段获得用户位置的精确估计。这一主动感知问题具有挑战性，最优解的求解需要在高维复杂函数空间中进行搜索，且该空间维度随测量次数增加。本文证明，长短期记忆（LSTM）网络可通过利用测量值间的潜在时间相关性，基于观测结果自动构建可扩展的信息向量（即隐藏状态）。随后，该状态向量通过深度神经网络（DNN）以无码本方式映射为下一时间帧的RIS配置。当所有测量完成时，另一DNN可将LSTM单元状态映射为用户设备（UE）的估计位置。数值结果表明，与现有主动及非主动方法相比，所提出的主动RIS设计能降低定位误差。该方案具有可解释性，并能泛化至感知阶段序列中的提前终止场景。