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.

翻译：在5G及未来通信系统中，不同用户设备间的硬件异构性为基于波束的通信带来了新的挑战。这种异构性限制了基于机器学习（ML）算法的适用性。本文强调，必须将硬件异构性视为ML辅助波束管理中的首要设计考量。我们分析了存在异构性时的关键失效模式，并通过案例研究展示了其对性能的影响。最后，我们探讨了提升波束管理泛化能力的潜在策略。