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

arXiv: https://arxiv.org/abs/2309.10575 · HTML: https://mlsanity.com/paper/2309.10575/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2023.

## Built on by (most cited first, 8 of 8)

- [LVM4CSI: Enabling Direct Application of Pre-Trained Large Vision Models for Wireless Channel Tasks](https://mlsanity.com/paper/2507.05121/) (2025)
- [Explainable and Robust Millimeter Wave Beam Alignment for AI-Native 6G Networks](https://mlsanity.com/paper/2501.17883/) (2025)
- [Multi-Modality Sensing in mmWave Beamforming for Connected Vehicles Using Deep Learning](https://mlsanity.com/paper/2504.06173/) (2025)
- [Digital Twin-Assisted Explainable AI for Robust Beam Prediction in mmWave MIMO Systems](https://mlsanity.com/paper/2507.14180/) (2025)
- [AI/ML Life Cycle Management for Interoperable AI Native RAN](https://mlsanity.com/paper/2507.18538/) (2025)
- [CAR-BRAINet: Sub-6GHz Aided Spatial Adaptive Beam Prediction with Multi Head Attention for Heterogeneous Vehicular Networks](https://mlsanity.com/paper/2509.10508/) (2025)
- [Multi-Modal Sensing and Fusion in mmWave Beamforming for Connected Vehicles: A Transformer Based Framework](https://mlsanity.com/paper/2602.13606/) (2026)
- [Structure-Aware Multimodal LLM Framework for Trustworthy Near-Field Beam Prediction](https://mlsanity.com/paper/2603.16143/) (2026)

## Abstract (arXiv)

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.