Foundation Model-Aided Hierarchical Control for Robust RIS-Assisted Near-Field Communications

TL;DR

This paper introduces a hierarchical control framework using dual-transformer models for RIS-assisted near-field communications in 6G, enhancing spectral efficiency and blockage prediction accuracy amid dynamic conditions.

Contribution

It proposes a dual-transformer hierarchical deep reinforcement learning framework that jointly handles rapid CSI estimation and blockage prediction for robust near-field communications.

Findings

Improves spectral efficiency by ~18% over baselines.

Achieves a blockage prediction F1-score of 0.92.

Provides a 769 ms advance warning for blockages.

Abstract

The deployment of extremely large aperture arrays (ELAAs) in sixth-generation (6G) networks could shift communication into the near-field communication (NFC) regime. In this regime, signals exhibit spherical wave propagation, unlike the planar waves in conventional far-field systems. Reconfigurable intelligent surfaces (RISs) can dynamically adjust phase shifts to support NFC beamfocusing, concentrating signal energy at specific spatial coordinates. However, effective RIS utilization depends on both rapid channel state information (CSI) estimation and proactive blockage mitigation, which occur on inherently different timescales. CSI varies at millisecond intervals due to small-scale fading, while blockage events evolve over seconds, posing challenges for conventional single-level control algorithms. To address this issue, we propose a dual-transformer (DT) hierarchical framework that integrates two specialized transformer models within a hierarchical deep reinforcement learning (HDRL) architecture, referred to as the DT-HDRL framework. A fast-timescale transformer processes ray-tracing data for rapid CSI estimation, while a vision transformer (ViT) analyzes visual data to predict impending blockages. In HDRL, the high-level controller selects line-of-sight (LoS) or RIS-assisted non-line-of-sight (NLoS) transmission paths and sets goals, while the low-level controller optimizes base station (BS) beamfocusing and RIS phase shifts using instantaneous CSI. This dual-timescale coordination maximizes spectral efficiency (SE) while ensuring robust performance under dynamic conditions. Simulation results demonstrate that our approach improves SE by approximately 18% compared to single-timescale baselines, while the proposed blockage predictor achieves an F1-score of 0.92, providing a 769 ms advance warning window in dynamic scenarios.