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When Movable Antennas Meet RSMA and RIS: Robust Beamforming Design With Channel Uncertainty

Muhammad Asif, Asim Ihsan, Zhongliang Wang, Manzoor Ahmed, Xingwang Li, Arumugam Nallanathan, Symeon Chatzinotas

TL;DR

This work addresses robust downlink transmission in a MAs-enabled RIS-assisted RSMA system with blocked direct links and bounded CSI errors. An alternating-optimization approach jointly optimizes transmit precoding, RIS phase shifts, and MA positions: precoding via SDP with S-procedure-based LMIs, RIS design via SCA/SDR with a worst-case CSI formulation, and MA positioning via SCA-BCD with distance-constraint linearization. The scheme guarantees QoS, adheres to power and mutual-coupling constraints, and demonstrates fast convergence with significant sum-rate gains over fixed-antenna benchmarks, even under substantial channel uncertainty. The results substantiate the benefits of integrating movable antennas, RIS configurability, and RSMA for robust, high-efficiency 6G downlink communications.

Abstract

In this work, we propose an intelligent optimization framework for a multi-user communication system integrating movable antennas (MAs) and a reconfigurable intelligent surface (RIS) under the rate-splitting multiple access (RSMA) protocol. The system sum-rate is maximized through joint optimization of transmit precoding vectors, RIS reflection matrix, common-rate allocation, and MA positions, subject to quality-of-service (QoS), power-budget, common-rate decoding, and mutual coupling constraints. Imperfect channel state information (CSI) is considered for all links, where robustness is ensured by modeling channel estimation errors within a bounded uncertainty region, guaranteeing worst-case performance reliability. The resulting non-convex problem is solved using an alternating optimization framework. The precoding subproblem is reformulated as a semidefinite programming (SDP) problem via linear matrix inequalities derived using the S-procedure. The RIS reflection matrix is optimized using successive convex approximation (SCA), yielding an equivalent SDP formulation. The MA position optimization is addressed through SCA combined with block coordinate descent (BCD) method. Numerical results validate the effectiveness of the proposed framework and demonstrate fast convergence.

When Movable Antennas Meet RSMA and RIS: Robust Beamforming Design With Channel Uncertainty

TL;DR

This work addresses robust downlink transmission in a MAs-enabled RIS-assisted RSMA system with blocked direct links and bounded CSI errors. An alternating-optimization approach jointly optimizes transmit precoding, RIS phase shifts, and MA positions: precoding via SDP with S-procedure-based LMIs, RIS design via SCA/SDR with a worst-case CSI formulation, and MA positioning via SCA-BCD with distance-constraint linearization. The scheme guarantees QoS, adheres to power and mutual-coupling constraints, and demonstrates fast convergence with significant sum-rate gains over fixed-antenna benchmarks, even under substantial channel uncertainty. The results substantiate the benefits of integrating movable antennas, RIS configurability, and RSMA for robust, high-efficiency 6G downlink communications.

Abstract

In this work, we propose an intelligent optimization framework for a multi-user communication system integrating movable antennas (MAs) and a reconfigurable intelligent surface (RIS) under the rate-splitting multiple access (RSMA) protocol. The system sum-rate is maximized through joint optimization of transmit precoding vectors, RIS reflection matrix, common-rate allocation, and MA positions, subject to quality-of-service (QoS), power-budget, common-rate decoding, and mutual coupling constraints. Imperfect channel state information (CSI) is considered for all links, where robustness is ensured by modeling channel estimation errors within a bounded uncertainty region, guaranteeing worst-case performance reliability. The resulting non-convex problem is solved using an alternating optimization framework. The precoding subproblem is reformulated as a semidefinite programming (SDP) problem via linear matrix inequalities derived using the S-procedure. The RIS reflection matrix is optimized using successive convex approximation (SCA), yielding an equivalent SDP formulation. The MA position optimization is addressed through SCA combined with block coordinate descent (BCD) method. Numerical results validate the effectiveness of the proposed framework and demonstrate fast convergence.
Paper Structure (12 sections, 63 equations, 10 figures, 1 algorithm)

This paper contains 12 sections, 63 equations, 10 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Optimization Problem Formulation
  3. Channel Model
  4. Signal Model
  5. Problem Formulation
  6. Proposed Solution for the Optimization Problem
  7. Updating Precoding Vectors $\mathbf{p}_{c}$ and $\mathbf{p}_{m}$
  8. Updating RIS Reflection Matrix $\mathbf \Phi$
  9. Updating MAs Positions $\widehat{\mathbf{t}}$
  10. Complexity Analysis of the Proposed Robust Algorithm
  11. Simulation Results and Performance Analysis
  12. Conclusion

Figures (10)

  • Figure 1: System model.
  • Figure 2: Simulation environment.
  • Figure 3: System convergence for different number of RIS elements.
  • Figure 4: System convergence for different number transmit and receive paths.
  • Figure 5: System convergence for different levels of channel uncertainty.
  • ...and 5 more figures