Wireless Environment Information Sensing, Feature, Semantic, and Knowledge: Four Steps Towards 6G AI-Enabled Air Interface

TL;DR

This paper introduces a four-step framework for wireless environment information sensing to enhance 6G AI-enabled air interfaces, improving real-time adaptation, reducing pilot overhead, and supporting channel prediction.

Contribution

The paper proposes a novel four-step WEI framework for real-time environment sensing, enabling proactive 6G air interface optimization and demonstrating its effectiveness through a path loss prediction case study.

Findings

Model inference time is only 2.2 ms for environment knowledge extraction.

WEI reduces pilot overhead by 25%.

Framework supports real-time channel prediction and adaptation.

Abstract

The air interface technology plays a crucial role in optimizing the communication quality for users. To address the challenges brought by the radio channel variations to air interface design, this article proposes a framework of wireless environment information-aided 6G AI-enabled air interface (WEI-6G AI$^{2}$), 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 AI$^{2}$ 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. To validate the availability and compare the effect of different types of WEI, a path loss prediction use case is designed. The results demonstrate that leveraging environment knowledge requires only 2.2 ms of model inference time, which can effectively support real-time design for future 6G AI$^{2}$. Additionally, WEI can reduce the pilot overhead by 25\%. Finally, several open issues are pointed out, including multi-modal sensing data synchronization and information extraction method construction.