AI/ML for Beam Management in 5G-Advanced: A Standardization Perspective

TL;DR

This paper reviews the integration of AI/ML techniques into beam management for 5G-Advanced networks, highlighting standardization efforts, challenges, and potential improvements in accuracy, overhead, and latency.

Contribution

It provides a comprehensive overview of AI/ML-based beam management in 5G-Advanced, comparing legacy and new frameworks, and discusses standardization challenges and future directions.

Findings

AI/ML enhances beam management accuracy

Reduces overhead and latency in beam operations

Identifies key challenges in standardizing AI/ML protocols

Abstract

In beamformed wireless cellular systems such as 5G New Radio (NR) networks, beam management (BM) is a crucial operation. In the second phase of 5G NR standardization, known as 5G-Advanced, which is being vigorously promoted, the key component is the use of artificial intelligence (AI) based on machine learning (ML) techniques. AI/ML for BM is selected as a representative use case. This article provides an overview of the AI/ML for BM in 5G-Advanced. The legacy non-AI and prime AI-enabled BM frameworks are first introduced and compared. Then, the main scope of AI/ML for BM is presented, including improving accuracy, reducing overhead and latency. Finally, the key challenges and open issues in the standardization of AI/ML for BM are discussed, especially the design of new protocols for AI-enabled BM. This article provides a guideline for the study of AI/ML-based BM standardization.