Four Steps Toward 6G AI-Enabled Air Interface: Wireless Environmental Information Sensing, Feature, Semantics, and Knowledge

Air interface technology plays a crucial role in optimizing the quality of communication for users. To address the challenges brought by the radio channel variations to air interface design, this article proposes a framework of wireless environmental information-aided 6G AI-enabled air interface (WEI-6G AI²), which actively acquires real-time environment details to facilitate channel fading prediction and communication technology optimization. Specifically, we first outline the role of WEI in supporting the 6G AI² in scenario adaptability, real-time inference, and proactive action. Then, WEI is delineated into four progressive steps: raw sensing data, features obtained by data dimensionality reduction, semantics tailored to tasks, and knowledge that quantifies the environmental impact on the channel. A path loss prediction use case has been designed to validate the availability and compare the effects of different types of WEI. The results demonstrate that leveraging environment knowledge requires only 2.3 ms of model inference time, which can effectively support real-time design for future 6G AI². Additionally, WEI can reduce the pilot overhead by 25 percent. Finally, several open issues are pointed out, including multi-modal sensing data synchronization, computational efficiency, and system integration.