Rethinking Beam Management: Generalization Limits Under Hardware Heterogeneity
This addresses a domain-specific challenge for 5G communication systems, but it is incremental as it focuses on highlighting issues rather than presenting new solutions.
The paper tackles the problem of hardware heterogeneity limiting machine learning-based beam management in 5G and beyond, analyzing failure modes and discussing strategies to improve generalization.
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.