针对5G设备硬件差异，提出改进波束管理泛化能力的新思路
Rethinking Beam Management: Generalization Limits Under Hardware Heterogeneity
- 将硬件异构性视为波束管理设计核心问题
- 揭示异构性导致的算法失效关键模式
- 适合通信系统设计与机器学习应用者参考
5G及未来通信中，用户设备间的硬件异构性给基于波束的通信带来新挑战，限制了机器学习（ML）算法的应用。本文强调必须将硬件异构性作为机器学习辅助波束管理的一级设计考量。通过分析异构性下的关键失败模式，并结合案例研究展示其对性能的影响，最后探讨提升波束管理泛化能力的潜在策略。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。