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Adaptive Channel Estimation and Hybrid Beamforming for RIS aided Vehicular Communication

Tianyou Li, Haifeng Hu, Dapeng Li

TL;DR

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 and demonstrates substantial performance gains in channel estimation efficiency, beamforming robustness, and system throughput compared to conventional schemes.

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.

Adaptive Channel Estimation and Hybrid Beamforming for RIS aided Vehicular Communication

TL;DR

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 and demonstrates substantial performance gains in channel estimation efficiency, beamforming robustness, and system throughput compared to conventional schemes.

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.
Paper Structure (16 sections, 52 equations, 7 figures)

This paper contains 16 sections, 52 equations, 7 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Adaptive Channel Estimation Framework
  4. Small Timescale Channel Estimation
  5. Large Timescale Channel Estimation
  6. Hybrid Beamforming Optimization with the Estimated Channels
  7. Single VUE in Narrowband System
  8. Optimization of $\bm{F}$
  9. Optimization of $\bar{{\theta}}_i$
  10. Multi VUEs in Broadband System
  11. Optimization of Resource Allocation While Fixing the Beamforming Settings
  12. Optimization of Hybrid Beamforming While Fixing the Resource Settings
  13. Simulation Results
  14. Conclusion
  15. Proof of Theorem 1
  16. ...and 1 more sections

Figures (7)

  • Figure 1: A transparent RIS is deployed on the roof of the vehicle and multiple antenna are deployed at both sides of transmitter and receiver. The RIS can be controlled by the in-vehicle controller via the wireless backhaul link.
  • Figure 2: NMSE versus number of time blocks $I$ for RIS-aided vehicular MIMO communication with $M =100$.
  • Figure 3: (a) Convergency of the proposed algorithm in outer iterations. (b) Convergency of the proposed algorithm in inner iterations
  • Figure 4: Data rate versus number of time blocks $I$ for RIS-aided vehicular MIMO communication with $M=100$.
  • Figure 5: Achievable rate versus the speed of vehicle for RIS-aided vehicular MIMO communication.
  • ...and 2 more figures