The field of wireless communications is witnessing significant advancements with the integration of movable antenna technology. Recent research has focused on optimizing antenna positions to improve system performance, enhance physical layer security, and increase energy harvesting efficiency. Notably, the development of algorithms that jointly optimize antenna positions and beamforming has shown promising results in maximizing sum rates and minimizing interference. Furthermore, the application of machine learning techniques, such as Transformer-based frameworks, has improved the prediction of optimal antenna positions, thereby enhancing secrecy performance. Additionally, research on simultaneous wireless information and power transfer has demonstrated the potential of fluid antenna systems in improving energy harvesting efficiency. Overall, these advancements are paving the way for more efficient, secure, and reliable wireless communication systems.

Some noteworthy papers in this regard include: ProxySelect, which proposes a scalable and frequency selectivity-aware user scheduling algorithm for joint OFDMA and MU-MIMO usage in 802.11ax WiFi. RoleAware-MAPP, a novel Transformer-based framework that incorporates domain knowledge to predict optimal movable antenna positions for securing wireless communications.