Channel Uncertainty-Aware Robust Beamforming for RIS-Assisted RSMA Communication With Movable Antennas

TL;DR

This paper proposes a robust joint optimization framework for RIS-assisted RSMA communication systems with movable antennas, addressing channel uncertainty and demonstrating significant performance improvements.

Contribution

It introduces a novel joint optimization approach for transmit precoding, RIS reflection, and antenna positions under imperfect CSI, enhancing robustness and system performance.

Findings

Achieves significant sum-rate improvements over benchmarks.

Ensures robustness against channel estimation errors.

Demonstrates fast and stable convergence in simulations.

Abstract

This work investigates a robust resource allocation framework for a downlink multi-user communication system integrating movable antennas (MAs) and reconfigurable intelligent surfaces (RISs) under the rate-splitting multiple access (RSMA) transmission protocol. Unlike conventional fixed-position antenna architectures, the considered MAs-enabled system introduces spatially adaptive channel variations in which antenna positions directly influence the effective channel responses. Consequently, under imperfect channel state information (CSI), the impact of CSI uncertainty propagates not only through active and passive beamforming design, but also through the antenna position optimization process, leading to a highly coupled robust optimization problem. To address this challenge, we formulate a system sum-rate maximization problem by jointly optimizing the transmit precoding vectors, RIS reflection matrix, common-rate allocation, and MAs positions, subject to quality-of-service (QoS), power-budget, common-rate decoding, and mutual coupling constraints. The resulting non-convex problem is efficiently handled through an iterative robust optimization framework, where the original problem is successively decomposed into active beamforming, RIS reflection matrix, and MAs position optimization subproblems, and tractable convex surrogate functions are constructed to enable iterative optimization. Moreover, system robustness is ensured by incorporating a bounded CSI uncertainty model that explicitly captures channel estimation errors and guarantees reliable communication performance under worst-case channel conditions. Finally, extensive simulation results demonstrate that the proposed framework achieves significant performance gains and enhanced robustness compared with benchmark schemes, while also exhibiting fast and stable convergence behavior under practical imperfect CSI conditions.