Rethinking Beam Management: Generalization Limits Under Hardware Heterogeneity

TL;DR

This paper examines how hardware differences across devices affect ML-based beam management in 5G, emphasizing the importance of addressing heterogeneity to improve generalization and performance.

Contribution

It highlights hardware heterogeneity as a crucial factor in beam management, analyzes failure modes, and discusses strategies to enhance ML model generalization across diverse devices.

Findings

Hardware heterogeneity limits ML beam management effectiveness

Identified key failure modes caused by device differences

Proposed strategies to improve model generalization

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.