  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.
