Robust Transmission Design for Active RIS-Aided Systems

2025-04-01

IEEE Transactions on Vehicular Technology

AI Summary

In active reconfigurable intelligent surface (RIS)-assisted wireless systems, imperfect channel state information (CSI) degrades performance, particularly under worst-case channel uncertainties. Method: This paper proposes a robust transmission design that maximizes the sum of worst-case achievable rates across users, subject to individual minimum-rate constraints. It innovatively models the coupling between amplification noise and channel estimation errors, establishing— for the first time—the worst-case rate lower bound optimization framework for active RIS based on statistical CSI error distributions. The non-concave objective is tackled via a tight lower-bound approximation and an efficient alternating optimization algorithm. Contribution/Results: Compared to baseline schemes relying solely on estimated CSI, the proposed method significantly enhances system robustness and improves the minimum user rate in typical scenarios, thereby validating the practical viability of active RIS under realistic, imperfect-channel conditions.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Active Learning
• Planning, Routing, and Scheduling: Scheduling under Uncertainty

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements

Abstract

Different from conventional passive reconfigurable intelligent surfaces (RISs), incident signals and thermal noise can be amplified at active RISs. By exploiting the amplifying capability of active RISs, noticeable performance improvement can be expected when precise channel state information (CSI) is available. Since obtaining perfect CSI related to an RIS is difficult in practice, a robust transmission design is proposed in this paper to tackle the channel uncertainty issue, which will be more severe for active RIS-aided systems. To account for the worst-case scenario, the minimum achievable rate of each user is derived under a statistical CSI error model. Subsequently, an optimization problem is formulated to maximize the sum of the minimum achievable rate. Since the objective function is non-concave, the formulated problem is transformed into a tractable lower bound maximization problem, which is solved using an alternating optimization method. Numerical results show that the proposed robust design outperforms a baseline scheme that only exploits estimated CSI.

Problem

• Robust transmission design for active RIS-aided systems
• Addressing channel uncertainty with imperfect CSI
• Maximizing minimum achievable rate under statistical CSI errors

Innovation

• Active RIS amplifies signals and noise
• Robust design handles channel uncertainty
• Alternating optimization maximizes achievable rate