Active Sensing for Multiuser Beam Tracking with Reconfigurable Intelligent Surface

TL;DR

This paper introduces a deep learning-based active sensing framework using RNNs and GNNs to optimize beam tracking in multiuser RIS-assisted communication systems, significantly improving performance over existing methods.

Contribution

It proposes a novel deep learning approach combining RNNs and GNNs for adaptive beam tracking in multiuser RIS systems, addressing the challenge of active channel sensing.

Findings

Significant performance gains over nonadaptive schemes.

Effective summarization of time-varying CSI with RNNs.

Improved beamforming and reflection coefficient configuration.

Abstract

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.