Adaptive Channel Estimation and Hybrid Beamforming for RIS aided Vehicular Communication

TL;DR

This paper introduces an adaptive channel estimation and hybrid beamforming framework for RIS-assisted vehicular MIMO systems, effectively handling high mobility and Doppler effects to improve communication performance.

Contribution

It proposes a velocity-aware pilot scheme with tensor decomposition and adaptive grouping, along with low-complexity hybrid beamforming algorithms for single and multi-VUE systems, enhancing efficiency and robustness.

Findings

Significant reduction in training overhead for channel estimation.

Improved system throughput and robustness under high mobility.

Enhanced beamforming accuracy and spectral efficiency.

Abstract

Reconfigurable intelligent surface (RIS) constitutes a disruptive technology for enhancing vehicular communication performance through reconfigurable propagation environments. In this paper, we propose an adaptive channel estimation framework and hybrid beamforming optimization strategy for RIS-aided vehicular multiple-input multiple-output (MIMO) systems operating in high-mobility scenarios. To address severe Doppler effects and rapid channel variations, we design a velocity-aware pilot scheme that progressively estimates cascaded channels across two timescales, leveraging tensor decomposition and adaptive grouping of passive elements. This framework dynamically balances channel estimation accuracy and spectral efficiency, significantly reducing training overhead. Furthermore, we develop a low-complexity hybrid beamforming algorithm for both narrowband single vehicle user equipment (VUE) and broadband multi-VUE systems. For single-VUE scenarios, we derive closed-form active beamforming solutions and optimize passive beamforming via alternating optimization. For multi-VUE broadband systems, we jointly optimize subcarrier allocation, power distribution, and beamforming to maximize system throughput while mitigating inter-carrier interference (ICI) caused by Doppler spread, subject to quality-of-service (QoS) constraints and RIS hardware limitations. Our simulation results demonstrate that the proposed methods achieve substantial performance gains in channel estimation efficiency, beamforming robustness, and system throughput compared to conventional schemes, particularly under high mobility conditions.