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Achievable Rate Optimization for Reconfigurable Intelligent Surface-Aided Multi-User Movable Antenna Systems.

Liji Yu¹, Yuhui Ren¹

¹School of Information Science and Technology, Northwest University, Xi'an 710127, China.

Sensors (Basel, Switzerland)

|August 14, 2025

Summary

This study introduces an optimization framework for reconfigurable intelligent surface (RIS)-aided movable antenna (MA) systems. The novel approach enhances spectral efficiency by optimizing beamforming and antenna positions, outperforming fixed-position schemes.

Keywords:

antenna position optimization beamforming movable antenna reconfigurable intelligent surface (RIS)sequential quadratic programming (SQP)

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Area of Science:

Wireless Communications
Optimization Theory
Signal Processing

Background:

Reconfigurable intelligent surfaces (RIS) and movable antennas (MA) offer potential for enhanced wireless communication systems.
Joint optimization of beamforming and antenna positions in RIS-aided MA systems is complex due to nonlinear spatial constraints.
Existing methods, often relying on gradient search, do not fully exploit the degrees of freedom offered by movable antennas.

Purpose of the Study:

To propose a novel optimization framework for RIS-aided MA systems.
To address the joint optimization of beamforming and antenna positions.
To improve spectral efficiency by leveraging the combined benefits of RIS and MA technologies.

Main Methods:

A novel optimization framework is developed for RIS-aided MA systems.
Antenna positioning is reformulated as a sequential quadratic programming (SQP) problem to handle nonlinear spatial constraints.
An alternating optimization scheme decouples the problem into beamforming (using maximum ratio transmission and fixed-point iteration) and antenna location optimization (via SQP).

Main Results:

The proposed method significantly enhances spectral efficiency.
A performance improvement of approximately 25% is achieved compared to fixed-position schemes.
The SQP-based approach effectively exploits the positional degrees of freedom of movable antennas, surpassing gradient search methods.

Conclusions:

The proposed optimization framework effectively integrates RIS and MA technologies for superior wireless communication performance.
The SQP reformulation provides an efficient solution for optimizing antenna positions under complex spatial constraints.
This work demonstrates a more effective approach to exploiting the full potential of movable antennas in intelligent surface-aided systems.