AI 中文总结
本综述针对可移动、流体、捏合三类空间可重构天线系统，梳理了深度学习等各类AI技术的应用，探讨了相关开放挑战与未来方向。
AI 中文摘要
近年来，第六代（6G）无线网络已从固定阵列设计转向可根据特定环境条件调整空间配置的天线架构。可移动天线、流体天线和捏合天线系统以不同方式体现了这一原理，但它们共享一个共同愿景：将空间灵活性作为额外自由度（DoF），以提升通信、感知、安全性和资源效率。然而，这些新技术也带来了具有挑战性的问题，因为天线配置必须与信道获取、波束成形、移动性和网络资源管理共同考虑。因此，人工智能（AI）已成为学习这些高度耦合系统快速自适应控制策略的重要工具。在本综述中，我们对空间可重构天线系统中的AI应用进行了统一综述。我们首先介绍可移动、流体和捏合天线的基本原理，随后根据主要优化目标总结最新的AI驱动设计。此外，我们比较了深度学习（DL）、深度强化学习（DRL）、多智能体强化学习（MARL）、图学习、Transformer、大语言模型（LLMs）以及结构引导学习在不同天线架构中的作用。最后，我们讨论了可扩展、鲁棒且硬件感知的智能可重构天线网络面临的开放挑战和未来方向。
英文摘要
Recently, sixth-generation (6G) wireless networks have moved beyond fixed-array designs toward antenna architectures that can adapt their spatial configuration to specific environmental conditions. Movable antenna, fluid antenna, and pinching antenna systems represent this principle in different ways, but they share a common vision: exploiting spatial flexibility as an additional degree of freedom (DoF) to improve communication, sensing, security, and resource efficiency. These new techniques, however, also bring challenging problems, as antenna configuration must be jointly considered with channel acquisition, beamforming, mobility, and network resource management. Therefore, artificial intelligence (AI) has become an important tool for learning fast and adaptive control policies for these highly coupled systems. In this survey, we provide a unified review of AI for spatially reconfigurable antenna systems. We first introduce the basic principles of movable, fluid, and pinching antennas, which is followed by a summary of the latest AI-enabled designs according to their primary optimization objectives. Furthermore, we compare the roles of deep learning (DL), deep reinforcement learning (DRL), multi-agent reinforcement learning (MARL), graph learning, Transformers, large language models (LLMs), and structure-guided learning across different antenna architectures. Finally, we discuss open challenges and future directions toward scalable, robust, and hardware-aware intelligent reconfigurable antenna networks.
Comments30 pages, 7 figures, submitted to IEEE COMST
