Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks

TL;DR

This position paper discusses the early-stage integration of AI/ML techniques into wireless network air interfaces, emphasizing the need for interdisciplinary research and outlining key challenges and opportunities.

Contribution

It provides an overview of AI/ML applications in wireless air interfaces, highlighting standardization efforts, use cases, and open research challenges.

Findings

AI/ML in wireless air interfaces is still emerging.

Standardization efforts are underway in 3GPP.

Open research challenges include technical requirements and architecture.

Abstract

AI/ML research has predominantly been driven by domains such as computer vision, natural language processing, and video analysis. In contrast, the application of AI/ML to wireless networks, particularly at the air interface, remains in its early stages. Although there are emerging efforts to explore this intersection, fully realizing the potential of AI/ML in wireless communications requires a deep interdisciplinary understanding of both fields. We provide an overview of AI/ML-related discussions in 3GPP standardization, highlighting key use cases, architectural considerations, and technical requirements. We outline open research challenges and opportunities where academic and industrial communities can contribute to shaping the future of AI-enabled wireless systems.