Rethinking Beam Management: Generalization Limits Under Hardware Heterogeneity
🤖 AI Summary
Hardware heterogeneity severely limits the generalization capability of machine learning–based beam management algorithms across diverse user equipment. This work is the first to treat hardware heterogeneity as a first-class design consideration in beam management, systematically analyzing the critical failure modes it induces. By integrating communication system modeling with case studies, the study quantifies the adverse impact of hardware disparities on model performance, revealing their significant negative effect on beam prediction accuracy. Building on these insights, the paper proposes effective strategies to enhance model generalization, offering a novel pathway toward robust beam management suitable for real-world deployment scenarios.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
beam management
hardware heterogeneity
machine learning
generalization
5G
Innovation
Methods, ideas, or system contributions that make the work stand out.
hardware heterogeneity
beam management
machine learning
generalization
5G
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