Rethinking Beam Management: Generalization Limits Under Hardware Heterogeneity

Fuente: arXiv
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Autores principales: Zeulin, Nikita, Galinina, Olga, Kilinc, Ibrahim, Andreev, Sergey, Heath Jr, Robert W.
Formato: Preprint
Publicado: 2026
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author Zeulin, Nikita
Galinina, Olga
Kilinc, Ibrahim
Andreev, Sergey
Heath Jr, Robert W.
author_facet Zeulin, Nikita
Galinina, Olga
Kilinc, Ibrahim
Andreev, Sergey
Heath Jr, Robert W.
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2602_18151
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Rethinking Beam Management: Generalization Limits Under Hardware Heterogeneity
Zeulin, Nikita
Galinina, Olga
Kilinc, Ibrahim
Andreev, Sergey
Heath Jr, Robert W.
Networking and Internet Architecture
Information Theory
Machine Learning
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.
title Rethinking Beam Management: Generalization Limits Under Hardware Heterogeneity
topic Networking and Internet Architecture
Information Theory
Machine Learning
url https://arxiv.org/abs/2602.18151