This paper addresses an uplink localization problem in which a base station (BS) aims to locate a remote user with the help of reconfigurable intelligent surfaces (RISs). We propose a strategy in which the user transmits pilots sequentially and the BS adaptively adjusts the sensing vectors, including the BS beamforming vector and multiple RIS reflection coefficients based on the observations already made, to eventually produce an estimated user position. This is a challenging active sensing problem for which finding an optimal solution involves searching through a complicated functional space whose dimension increases with the number of measurements. 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 state vectors. Subsequently, the state vector is mapped to the sensing vectors for the next time frame via a deep neural network (DNN). A final DNN is used to map the state vector to the estimated user position. Numerical result illustrates the advantage of the active sensing design as compared to non-active sensing methods. The proposed solution produces interpretable results and is generalizable in the number of sensing stages. Remarkably, we show that a network with one BS and multiple RISs can outperform a comparable setting with multiple BSs.

翻译：本文研究了基站（BS）在可重构智能表面（RIS）辅助下对远程用户进行上行定位的问题。我们提出一种策略：用户依次发送导频信号，基站根据已有观测值自适应调整感知向量（包括基站波束成形向量和多个RIS反射系数），最终生成用户位置估计。这是一个具有挑战性的主动感知问题，其最优解需要在复杂函数空间中搜索，且该空间维度随测量次数增加而增长。我们证明长短期记忆（LSTM）网络可有效利用测量数据间的潜在时序相关性，自动构建可扩展的状态向量。随后，通过深度神经网络（DNN）将状态向量映射为下一时刻的感知向量，并采用另一DNN将状态向量映射为用户位置估计。数值结果验证了主动感知设计相较于非主动感知方法的优势。所提方案输出结果具有可解释性，且可推广至任意感知阶段数量。值得注意的是，单个基站配合多个RIS的网络配置在性能上可超越同等设置下采用多个基站的方案。