This paper studies a beam tracking problem in which an access point (AP), in collaboration with a reconfigurable intelligent surface (RIS), dynamically adjusts its downlink beamformers and the reflection pattern at the RIS in order to maintain reliable communications with multiple mobile user equipments (UEs). Specifically, the mobile UEs send uplink pilots to the AP periodically during the channel sensing intervals, the AP then adaptively configures the beamformers and the RIS reflection coefficients for subsequent data transmission based on the received pilots. This is an active sensing problem, because channel sensing involves configuring the RIS coefficients during the pilot stage and the optimal sensing strategy should exploit the trajectory of channel state information (CSI) from previously received pilots. Analytical solution to such an active sensing problem is very challenging. In this paper, we propose a deep learning framework utilizing a recurrent neural network (RNN) to automatically summarize the time-varying CSI obtained from the periodically received pilots into state vectors. These state vectors are then mapped to the AP beamformers and RIS reflection coefficients for subsequent downlink data transmissions, as well as the RIS reflection coefficients for the next round of uplink channel sensing. The mappings from the state vectors to the downlink beamformers and the RIS reflection coefficients for both channel sensing and downlink data transmission are performed using graph neural networks (GNNs) to account for the interference among the UEs. Simulations demonstrate significant and interpretable performance improvement of the proposed approach over the existing data-driven methods with nonadaptive channel sensing schemes.

翻译：本文研究了一种波束跟踪问题，其中接入点（AP）与可重构智能表面（RIS）协作，动态调整其下行波束成形器及RIS反射模式，以维持与多个移动用户设备（UE）的可靠通信。具体而言，在信道感知间隔期间，移动UE周期性地向AP发送上行导频，AP随后基于接收到的导频自适应配置后续数据传输的波束成形器及RIS反射系数。这是一个主动感知问题，因为信道感知涉及在导频阶段配置RIS系数，且最优感知策略应利用先前接收导频的信道状态信息（CSI）轨迹。对此类主动感知问题求解析解极具挑战性。本文提出一种基于循环神经网络（RNN）的深度学习框架，自动将从周期性接收导频中获得的时变CSI摘要为状态向量。这些状态向量随后被映射为AP波束成形器和RIS反射系数，以用于后续下行数据传输，以及下一轮上行信道感知的RIS反射系数。从状态向量到信道感知和下行数据传输的波束成形器及RIS反射系数的映射均采用图神经网络（GNN）实现，以考虑UE间的干扰。仿真结果表明，所提方法相较于现有采用非自适应信道感知方案的数据驱动方法，性能提升显著且具有可解释性。