机器学习赋能开放无线网络，提升性能与效率
ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities
- 融合机器学习优化频谱管理与资源分配
- 揭示开放架构下智能决策的关键挑战
- 适合关注智能通信网络的研究者与工程师
随着无线通信系统日益复杂，开放无线接入网（O-RAN）因其促进互操作性和成本效益而备受关注。分析RAN架构演进及O-RAN核心原则表明，机器学习（ML）在应对频谱管理、资源分配和安全等挑战中具有关键作用。本综述全面梳理了机器学习在O-RAN中的集成应用，强调其在提升网络性能与效率方面的变革潜力。通过分析现有文献，本文描述了当前ML在O-RAN中的应用现状，并指明未来研究方向，旨在帮助研究人员与行业相关方制定最优服务策略，推动智能无线网络的理解与发展。
原文摘要 · Abstract (English)
As wireless communication systems become more advanced, Open Radio Access Networks (O-RAN) stand out as a notable framework that promotes interoperability and cost-effectiveness. An examination of the progression of RAN architectures, as well as O-RAN's underlying principles, reveals the importance of machine learning (ML) in addressing various challenges, including spectrum management, resource allocation, and security. Hence, this survey provides a comprehensive overview of the integration of ML within O-RAN, highlighting its transformative potential in enhancing network performance and efficiency. This survey aims to describe the current status of ML applications in O-RAN while indicating possible directions for future research by analyzing existing literature. The findings aim to assist researchers and stakeholders in formulating optimal service strategies and advancing the understanding of intelligent wireless networks.
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