mlsanityData to 14 Oct 2026

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

arXiv 2309.10575 ↗First posted 19 Sep 2023cs.IT cs.SY eess.SYcited by 8

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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.

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

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 6; 2026: 2.

8 papers.

  1. 2025LVM4CSI: Enabling Direct Application of Pre-Trained Large Vision Models for Wireless Channel Taskscited by 8
  2. 2025Explainable and Robust Millimeter Wave Beam Alignment for AI-Native 6G Networkscited by 3
  3. 2025Multi-Modality Sensing in mmWave Beamforming for Connected Vehicles Using Deep Learningcited by 3
  4. 2025Digital Twin-Assisted Explainable AI for Robust Beam Prediction in mmWave MIMO Systemscited by 2
  5. 2025AI/ML Life Cycle Management for Interoperable AI Native RANcited by 2
  6. 2025CAR-BRAINet: Sub-6GHz Aided Spatial Adaptive Beam Prediction with Multi Head Attention for Heterogeneous Vehicular Networkscited by 0
  7. 2026Multi-Modal Sensing and Fusion in mmWave Beamforming for Connected Vehicles: A Transformer Based Frameworkcited by 0
  8. 2026Structure-Aware Multimodal LLM Framework for Trustworthy Near-Field Beam Predictioncited by 0