Abstract
This paper proposes a novel optimization framework for reconfigurable intelligent surface (RIS)-aided movable antenna (MA) systems, tackling the joint optimization problem of beamforming and antenna positions. Unlike traditional approaches, we reformulate the antenna positioning task as a sequential quadratic programming (SQP) problem, enabling efficient handling of nonlinear spatial constraints through iteratively solved quadratic subproblems. An alternating optimization scheme is adopted to decouple the overall problem into two subproblems: (1) optimal beamforming using maximum ratio transmission (MRT) and fixed-point iteration, and (2) precise antenna location optimization via SQP. Simulation results demonstrate that the proposed method significantly enhances spectral efficiency by fully exploiting the synergistic benefits of RIS and MA technologies. The proposed method could achieve about a 25% performance improvement compared to the fixed-position scheme. Current approaches predominantly rely on gradient search methods, which fail to fully exploit the potential of positional DoFs. In contrast, our proposed method is more effective.