Fluid Antenna Systems (FAS) introduce a new degree of freedom for wireless networks by enabling the physical antenna position to adapt dynamically to changing radio conditions. While existing studies primarily emphasize physical-layer gains, their broader implications for network operation remain largely unexplored. Once antennas become reconfigurable entities, antenna positioning naturally becomes part of the network control problem rather than a standalone optimization task. This article presents an AI-native perspective on fluid antenna networks for future 6G systems. Instead of treating antenna repositioning as an isolated operation, we consider a closed-loop control architecture in which antenna adaptation is jointly managed with conventional radio resource management (RRM) functions. Within this framework, real-time network observations are translated into coordinated antenna and resource configuration decisions that respond to user mobility, traffic demand, and evolving interference conditions. To address the complexity of multi-cell environments, we explore a multi-agent reinforcement learning (MARL) approach that enables distributed and adaptive control across base stations. Illustrative results show that intelligent antenna adaptation yields consistent performance gains, particularly at the cell edge, while also reducing inter-cell interference. These findings suggest that the true potential of fluid antenna systems lies not only in reconfigurable hardware, but in intelligent network control architectures that can effectively exploit this additional spatial degree of freedom.

翻译：流体天线系统通过使物理天线位置动态适应变化的无线环境，为无线网络引入了新的自由度。现有研究主要强调物理层增益，但其对网络运行的更广泛影响仍基本未被探索。当天线成为可重构实体后，天线定位自然成为网络控制问题的一部分，而非独立的优化任务。本文针对未来6G系统，提出一种面向流体天线网络的AI原生视角。我们不再将天线重定位视为孤立操作，而是考虑一种闭环控制架构，在该架构中天线自适应与传统的无线资源管理功能协同管理。在此框架下，实时网络观测被转化为协调的天线和资源配置决策，以响应移动性、流量需求及不断变化的干扰条件。为应对多小区环境的复杂性，我们探索了一种多智能体强化学习方法，使基站间能够实现分布式自适应控制。示例结果表明，智能天线自适应能带来持续的性能增益，尤其在小区边缘，同时还能降低小区间干扰。这些发现表明，流体天线系统的真正潜力不仅在于可重构硬件，更在于能够有效利用这一额外空间自由度的智能网络控制架构。