AISAC：为6G闭合人工智能与集成传感通信之间的环路
AISAC: Closing the Loop Between AI and Integrated Sensing and Communication for 6G
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中文总结 AI 辅助
研究针对6G中人工智能与集成传感通信融合问题，提出AISAC闭环框架，阐述其新物理层设计原则，展示全貌、解释问题、开发架构并概述用例及相关开放问题。
中文摘要 AI 辅助
在国际电联IMT-2030对第六代（6G）网络的愿景中，集成传感与通信（ISAC）和人工智能与通信（AIAC）被视为独立的使用场景。然而实际上，这两个方向已开始融合。ISAC使网络能够观察物理世界，而AI让网络能从这些观察中学习并据此行动。本文引入了人工智能集成传感与通信（AISAC）作为这种融合的闭环框架。在AISAC中，AI不仅是优化ISAC系统的工具，ISAC也是AI接收数据、上下文和连接性的物理基础。关键技术要点是AISAC需要新的物理层设计原则，其中ISAC波形、波束、功率、带宽和传感模式应配置用于学习对齐，而非仅用于传感失真或通信速率。我们展示了AISAC的全貌，解释了不完美传感如何改变学习问题，开发了闭环架构及其传感-通信-学习三方的张力，并概述了一个车辆边缘智能用例以及理论、实施和标准化方面的开放问题。
英文摘要
Integrated sensing and communication (ISAC) and AI-and-communication (AIAC) are identified as separate usage scenarios in the ITU IMT-2030 vision for sixth-generation (6G) networks. In practice, however, these two directions are already beginning to merge. ISAC gives the network a way to observe the physical world, while AI gives the network a way to learn from those observations and act on them. This article introduces AI-integrated sensing and communication (AISAC) as a closed-loop framework for this merger. In AISAC, AI is not only a tool used to optimize an ISAC system. ISAC is also the physical substrate through which AI receives data, context, and connectivity. The key technical message is that AISAC requires a new physical-layer design principle, in which the ISAC waveform, beam, power, bandwidth, and sensing mode should be configured for learning alignment, not for sensing distortion or communication rate alone. In particular, the sensing configuration that is most accurate from a classical estimation viewpoint need not be the one that is most useful for training or inference. We present the AISAC landscape, explain why imperfect sensing changes the learning problem, develop the closed-loop architecture and its three-way sensing-communication-learning tension, and outline a vehicular edge-intelligence use case together with open problems for theory, implementation, and standardization.
