The fifth-generation (5G) network is considered a game-changing technology that promises advanced connectivity for businesses and growth opportunities. To gain a comprehensive understanding of this research domain, it is essential to scrutinize past research to investigate 5G-radio access network (RAN) architecture components and their interaction with computing tasks. This systematic literature review focuses on articles related to the past decade, specifically on machine learning models integrated with 5G-RAN architecture. The review disregards service types like the Internet of Medical Things, Internet of Things, and others provided by 5G-RAN. The review utilizes major databases such as IEEE Xplore, ScienceDirect, and Web of Science to locate highly cited peer-reviewed studies among 785 articles. After implementing a two-phase article filtration process, 143 articles are categorized into review articles (15/143) and learning-based development articles (128/143) based on the type of machine learning used in development. Motivational topics are highlighted, and recommendations are provided to facilitate and expedite the development of 5G-RAN. This review offers a learning-based mapping, delineating the current state of 5G-RAN architectures (e.g., O-RAN, C-RAN, HCRAN, and F-RAN, among others) in terms of computing capabilities and resource availability. Additionally, the article identifies the current concepts of ML prediction (categorical vs. value) that are implemented and discusses areas for future enhancements regarding the goal of network intelligence.

中文翻译：

---

机器学习在 5G-RAN 架构中的应用：问题、挑战和未来方向的全面系统综述


第五代 （5G） 网络被认为是一种改变游戏规则的技术，有望为企业提供先进的连接和增长机会。为了全面了解这一研究领域，必须仔细研究过去的研究，以研究 5G 无线接入网络 （RAN） 架构组件及其与计算任务的交互。本系统性文献综述侧重于与过去十年相关的文章，特别是与 5G-RAN 架构集成的机器学习模型。该审查忽略了医疗物联网、物联网等服务类型以及 5G-RAN 提供的其他服务类型。该综述利用 IEEE Xplore、ScienceDirect 和 Web of Science 等主要数据库在 785 篇文章中查找高引用的同行评审研究。在实施两阶段文章筛选过程后，根据开发中使用的机器学习类型，将 143 篇文章分为评论文章 （15/143） 和基于学习的开发文章 （128/143）。重点介绍了激励性主题，并提供了促进和加快 5G-RAN 开发的建议。这篇综述提供了一个基于学习的映射，描述了 5G-RAN 架构（例如 O-RAN、C-RAN、HCRAN 和 F-RAN 等）在计算能力和资源可用性方面的现状。此外，本文还确定了当前已实施的 ML 预测概念（分类与价值），并讨论了有关网络智能目标的未来增强领域。