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[2602.18151] Rethinking Beam Management: Generalization Limits Under Hardware Heterogeneity
Summary
This article discusses the challenges posed by hardware heterogeneity in beam-based communication systems for 5G and beyond, emphasizing the need for machine learning algorithms to adapt to diverse user devices.
Why It Matters
As 5G technology evolves, understanding how hardware differences affect communication is crucial for developing effective beam management strategies. This research highlights the importance of integrating hardware considerations into machine learning models, which can lead to improved performance and reliability in real-world applications.
Key Takeaways
- Hardware heterogeneity significantly impacts beam management in 5G systems.
- Machine learning algorithms must be designed with hardware diversity in mind.
- The article presents case studies illustrating the performance effects of this heterogeneity.
- Strategies for enhancing generalization in beam management are discussed.
- Addressing these challenges is critical for the future of communication technologies.
Computer Science > Networking and Internet Architecture arXiv:2602.18151 (cs) [Submitted on 20 Feb 2026] Title:Rethinking Beam Management: Generalization Limits Under Hardware Heterogeneity Authors:Nikita Zeulin, Olga Galinina, Ibrahim Kilinc, Sergey Andreev, Robert W. Heath Jr View a PDF of the paper titled Rethinking Beam Management: Generalization Limits Under Hardware Heterogeneity, by Nikita Zeulin and 4 other authors View PDF HTML (experimental) 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. Comments: Subjects: Networking and Internet Architecture (cs.NI); Information Theory (cs.IT); Machine Learning (cs.LG) Cite as: arXiv:2602.18151 [cs.NI] (or arXiv:2602.18151v1 [cs.NI] for this version) https://doi.org/10.48550/arXiv.2602.18151 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Nikita Zeulin [view email] [v1] Fri, 20 Feb 2026 11:30:13 UTC (822 KB) Full-text links: Access Paper: View a PDF of the paper title...