Rethinking Beam Management: Generalization Limits Under Hardware Heterogeneity

Nikita Zeulin; Olga Galinina; Ibrahim Kilinc; Sergey Andreev; Robert W. Heath

Rethinking Beam Management: Generalization Limits Under Hardware Heterogeneity

Networking and Internet Architecture 2026-02-23 v1 Information Theory Machine Learning math.IT

Authors: Nikita Zeulin , Olga Galinina , Ibrahim Kilinc , Sergey Andreev , Robert W. Heath

Abstract

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.

Keywords

unified modeling language network protocol machine learning

Cite

@article{arxiv.2602.18151,
  title  = {Rethinking Beam Management: Generalization Limits Under Hardware Heterogeneity},
  author = {Nikita Zeulin and Olga Galinina and Ibrahim Kilinc and Sergey Andreev and Robert W. Heath},
  journal= {arXiv preprint arXiv:2602.18151},
  year   = {2026}
}

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This work has been submitted to the IEEE for possible publication

Rethinking Beam Management: Generalization Limits Under Hardware Heterogeneity

Abstract

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