2024/09/28 by Jianhua Zhang, Zhang, Jianhua, Yu Li +14 · 2 citations
Computer Science · Engineering · #Distributed Sensor Networks and Detection Algorithms #FOS: Electrical engineering #Signal Processing (eess.SP) #UAV Applications and Optimization #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2409.19331
openalex publication_date 2024/09/28 · openalex created_date 2024/10/28 · openalex updated_date 2026/07/28
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 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. 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 AI2. 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.