AI-SEFAS: An AI-Driven Framework for Spectral Efficiency Optimization in Fluid Antenna Systems for 6G Networks

Abstract

This paper introduces AI-SEFAS (Artificial Intelligence-powered Spectral Efficiency Fluid Antenna System), an artificial intelligence-driven framework designed to optimize spectral efficiency (SE) in sixth-generation (6 G) wireless networks utilizing Fluid Antenna Systems (FAS). By integrating Convolutional Neural Networks (CNNs) for fast beamforming weight prediction and Deep Reinforcement Learning (DRL) for adaptive antenna repositioning, the proposed approach jointly addresses the challenges of dynamic user mobility, channel variations, and multi-user coordination. Unlike conventional methods that treat beamforming and antenna placement as separate problems, AI-SEFAS performs end-to-end optimization to maximize spectral utilization (bps/Hz) while effectively mitigating inter-cell interference in ultra-dense deployments. Extensive simulations under realistic 6 G scenarios demonstrate that AI-SEFAS achieves substantial gains in spectral efficiency, adaptability, and scalability compared to state-of-the-art Multiple-Input Multiple-Output (MIMO) and Reconfigurable Intelligent Surface (RIS)assisted systems. These results highlight the potential of AI-SEFAS as a key enabler for intelligent and flexible next-generation wireless networks.