Robust Transmission Design for RIS-Assisted High-Speed Train Communication Coverage Enhancement With Imperfect Cascaded Channels

2026-04-16

AI Summary

This work addresses the challenge of imperfect cascaded channel state information (CSI) in reconfigurable intelligent surface (RIS)-assisted high-speed train communications, where rapid channel time-variation and feedback delays severely degrade performance. For the first time, the uncertainty of the base station (BS)–RIS–user cascaded channel at the BS is explicitly modeled, and a robust optimization framework is developed under both bounded-error and statistical-error models. By leveraging the S-procedure, the worst-case non-convex constraints are transformed into linear matrix inequalities, while Bernstein-type inequalities are employed to convert outage probability constraints into second-order cone and linear constraints. Simulation results demonstrate that cascaded channel estimation errors have a significantly more detrimental impact on system performance than direct-link errors, and the proposed approach effectively enhances coverage robustness.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements

Abstract

Reconfigurable intelligent surface (RIS) has recently been gained attention as an effective technique improving the coverage and performance of communication systems by creating additional communication links. Deployment of RIS is crucial for overcoming signal coverage limitations, especially in high-speed train (HST) scenarios. Considerable research has been performed assuming perfect channel state information (CSI). However, due to the rapidly time-varying fading channels and feedback delays, achieving perfect CSI at the base station (BS) is not feasible in the HST scenarios. To tackle this problem, this paper investigates a robust design strategy for RIS-aided HST communication coverage enhancement, particularly focusing on cascaded BS-RIS-user channels at BS (CBRUB). The study explores the optimization problem under two types distinct of models: centered on minimizing transmit power subject to worst-case rate constraints within the bounded CSI error (BCSIE) model, and the other focusing on outage probability (OP) constraints under the statistical CSI error (SCSIE) model. We use the S-procedure to approximate the non-convex (NC) constraints, converting the worst-case rate constraints into linear matrix inequalities. Additionally, the Bernstein-type inequality is applied to transform the OP constraints into second-order cone constraints and linear inequalities. The simulation analysis results show that CBRUB errors have a significant effect on system performance compared to direct CSI errors.

Problem

• Reconfigurable Intelligent Surface
• High-Speed Train
• Imperfect CSI
• Cascaded Channels
• Robust Transmission

Innovation

• Reconfigurable Intelligent Surface (RIS)
• High-Speed Train (HST) Communications
• Imperfect Cascaded CSI
• Robust Optimization
• Outage Probability