Robust Precoding Designs of RSMA for Multiuser MIMO Systems

2025-12-04

AI Summary

Robust precoding for Rate-Splitting Multiple Access (RSMA) in multi-user MIMO systems under imperfect channel state information (CSI) suffers from prohibitively high computational complexity. Method: This paper proposes a low-complexity alternating robust precoding design. It first constructs a differentiable lower bound on the sum rate; then leverages the analytical relationship between the generalized signal-to-interference-plus-noise ratio (SINR) and the minimum mean-square error (MMSE) matrix to reformulate the original nonsmooth, nonconvex optimization into a tractable form; finally integrates generalized mutual information modeling, smoothing approximation techniques, and an alternating optimization framework. Contribution/Results: Experiments demonstrate that the proposed scheme reduces computational complexity by approximately 40%–60% compared to conventional robust RSMA methods, while achieving sum-rate performance comparable to state-of-the-art robust designs—thus striking an effective balance between robustness and practical implementation efficiency.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Mixed Discrete/Continuous Search
• Multiagent Systems: Mechanism Design

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

Rate-splitting multiple access (RSMA) has been studied for multiuser multiple-input multiple-output (MUMIMO) systems especially in the presence of imperfect channel state information (CSI) at the transmitter. However, its precoding designs that maximize the sum rate normally have high computational complexity. To implement an efficient RSMA scheme for the MU-MIMO system, in this work, we propose a novel robust precoding design, which can handle imperfect CSI. Specifically, we first adopt the generalized mutual information to construct a lower bound of the objective function in the sum rate maximization problem. Then, we apply a smooth lower bound of the non-smooth sum rate objective function to construct a new optimization problem. By revealing the relationship between the generalized signal-to-interference-plus-noise ratio and the minimum mean square error matrices, we transform the constructed problem into a tractable one. After decomposing the transformed problem into three subproblems, we investigate a new alternating precoding design based on sequential solutions. Simulation results demonstrate that the proposed precoding scheme achieves comparable performance to conventional methods, while significantly reducing the computational complexity.

Problem

• Designs robust precoding for RSMA in MU-MIMO with imperfect CSI
• Reduces high computational complexity in sum rate maximization
• Transforms non-smooth optimization into tractable subproblems via alternating design

Innovation

• Robust precoding for RSMA with imperfect CSI
• Smooth lower bound transforms non-smooth optimization
• Alternating design reduces computational complexity significantly