AISAC: Closing the Loop Between AI and Integrated Sensing and Communication for 6G

2026-07-17

AI Summary

This work proposes an AI-integrated sensing and communication (AISAC) framework tailored for 6G networks to address the longstanding challenge of harmonizing sensing, communication, and learning. By establishing the first closed-loop AISAC architecture, it introduces “learning alignment” as a novel physical-layer design paradigm that transcends conventional approaches centered solely on estimation error or communication rate. The framework jointly optimizes waveform, beamforming, power, bandwidth, and sensing modes within a tripartite coupled system of sensing, communication, and learning, leveraging edge intelligence in vehicular scenarios. It further elucidates the impact of imperfect sensing on AI performance, presents representative application cases, and identifies key open research challenges.

Abstract

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.

Problem

• AISAC
• Integrated Sensing and Communication
• 6G
• AI-and-communication
• learning alignment

Innovation

• AISAC
• learning alignment
• integrated sensing and communication
• AI-and-communication
• physical-layer design