• Computer Science > Networking and Internet Architecture [Submitted on 20 Feb 2026] Title:Rethinking Beam Management: Generalization Limits Under Hardware Heterogeneity 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. • Current browse context: cs.NI References & Citations export BibTeX citation Loading…
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- Summary
A recent study on 5G and beyond beam‑management highlights that hardware diversity across user devices severely limits the effectiveness of machine‑learning (ML) algorithms. The authors argue that heterogeneity must be treated as a primary design concern in ML‑aided beam selection. They identify key failure modes that arise when models trained on one device class are applied to others, and present case studies illustrating the resulting performance degradation. Finally, the paper outlines potential strategies-such as adaptive training, device‑aware feature engineering, and transfer‑learning techniques-to enhance generalization and improve beam‑management reliability in heterogeneous environments.
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