# Rethinking Beam Management: Generalization Limits Under Hardware Heterogeneity

arXiv: https://arxiv.org/abs/2602.18151 · HTML: https://mlsanity.com/paper/2602.18151/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 20 Feb 2026.

## Builds on (most cited first, 6 of 6)

- [Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues](https://mlsanity.com/paper/2303.08106/) (2023)
- [Physics-informed Generalizable Wireless Channel Modeling with Segmentation and Deep Learning: Fundamentals, Methodologies, and Challenges](https://mlsanity.com/paper/2401.01288/) (2024)
- [5G-Advanced AI/ML Beam Management: Performance Evaluation with Integrated ML Models](https://mlsanity.com/paper/2404.15326/) (2024)
- [Site-Specific Beam Alignment in 6G via Deep Learning](https://mlsanity.com/paper/2403.16186/) (2024)
- [Continual Learning for Wireless Channel Prediction](https://mlsanity.com/paper/2506.22471/) (2025)
- [ProtoBeam: Generalizing Deep Beam Prediction to Unseen Antennas using Prototypical Networks](https://mlsanity.com/paper/2501.03435/) (2025)

## Abstract (arXiv)

Hardware heterogeneity across diverse user devices poses new challenges for beam-based communication in 5G and beyond. This heterogeneity limits the applicability of machine learning (ML)-based algorithms. This article highlights the critical need to treat hardware heterogeneity as a first-class design concern in ML-aided beam management. We analyze key failure modes in the presence of heterogeneity and present case studies demonstrating their performance impact. Finally, we discuss potential strategies to improve generalization in beam management.