The sixth generation (6G) network is expected to deploy larger multiple-input multiple-output (MIMO) arrays to support massive connectivity, which will increase overhead and latency at the physical layer. Meanwhile, emerging 6G demands such as immersive communications and environmental sensing pose challenges to traditional signal processing. To address these issues, we propose the ``semantic-aware MIMO'' paradigm, which leverages specialist models and large models to perceive, utilize, and fuse the inherent semantics of channels and sources for improved performance. Moreover, for representative MIMO physical-layer tasks, e.g., random access activity detection, channel feedback, and precoding, we design specialist models that exploit channel and source semantics for better performance. Additionally, in view of the more diversified functions of 6G MIMO, we further explore large models as a scalable solution for multi-task semantic-aware MIMO and review recent advances along with their advantages and limitations. Finally, we discuss the challenges, insights, and prospects of the evolution of specialist models and large models empowered semantic-aware MIMO paradigms.

翻译：第六代（6G）网络预计将部署更大规模的多输入多输出（MIMO）阵列以支持海量连接，但这会增加物理层的开销与延迟。同时，沉浸式通信、环境感知等新兴6G需求对传统信号处理技术提出了挑战。为解决这些问题，我们提出“语义感知MIMO”范式，该范式利用专用模型与大型模型来感知、利用并融合信道与信源的内在语义以提升性能。此外，针对典型的MIMO物理层任务（如随机接入活动检测、信道反馈与预编码），我们设计了能够利用信道与信源语义以优化性能的专用模型。另外，鉴于6G MIMO功能日趋多样化，我们进一步探索将大型模型作为多任务语义感知MIMO的可扩展解决方案，并综述了相关最新进展及其优势与局限。最后，我们讨论了由专用模型与大型模型赋能的语义感知MIMO范式演进所面临的挑战、启示与前景。