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Multi-Agent Reinforcement Learning-Based Joint Optimization for Movable Antenna and RIS-Assisted Systems
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DOI：10.1109/LCOMM.2025.3641395.png)
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Abstract
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This letter investigates a multi-user multi-input single-output (MISO) system assisted by movable antennas (MAs) and the reconfigurable intelligent surface (RIS). Specifically, the positions of MAs and beamforming matrix at the base station (BS) together with the phase shifts at the RIS are jointly optimized to maximize the sum spectral efficiency (SE) over all users. To address the non-convex problem with coupled variables, we formulate the problem as a Markov decision process (MDP) and propose a multi-agent reinforcement learning (MARL)-based method which entails multiple agents for optimizing MA positions and one agent for optimizing BS beamforming and RIS phase shift matrix. Simulation results demonstrate that the proposed algorithm can effectively learn preferable strategies from wireless environments compared to other learning-based benchmarks, yielding significant improvements in sum SE in comparison with fixed-position antennas.
Keywords:
Movable antenna
reconfigurable intelligent surface
multi-agent reinforcement learning
beamforming matrix
phase shift matrix
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