A comprehensive systematic review on machine learning application in the 5G-RAN architecture: Issues, challenges, and future directions

IF 7.7 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Journal of Network and Computer Applications Pub Date : 2024-10-09 DOI:10.1016/j.jnca.2024.104041

Mohammed Talal , Salem Garfan , Rami Qays , Dragan Pamucar , Dursun Delen , Witold Pedrycz , Amneh Alamleh , Abdullah Alamoodi , B.B. Zaidan , Vladimir Simic

{"title":"A comprehensive systematic review on machine learning application in the 5G-RAN architecture: Issues, challenges, and future directions","authors":"Mohammed Talal , Salem Garfan , Rami Qays , Dragan Pamucar , Dursun Delen , Witold Pedrycz , Amneh Alamleh , Abdullah Alamoodi , B.B. Zaidan , Vladimir Simic","doi":"10.1016/j.jnca.2024.104041","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"233 ","pages":"Article 104041"},"PeriodicalIF":7.7000,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Network and Computer Applications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1084804524002182","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}

引用次数: 0

Abstract

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.

查看原文本刊更多论文

关于 5G-RAN 架构中机器学习应用的全面系统综述：问题、挑战和未来方向

第五代（5G）网络被认为是改变游戏规则的技术，有望为企业带来先进的连接性和增长机会。为了全面了解这一研究领域，有必要仔细研究过去对 5G 无线接入网络（RAN）架构组件及其与计算任务的交互作用的研究。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。本综述不涉及 5G-RAN 提供的医疗物联网、物联网等服务类型。综述利用 IEEE Xplore、ScienceDirect 和 Web of Science 等主要数据库，在 785 篇文章中找到了高引用率的同行评审研究。在对文章进行两阶段过滤后，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）。其中突出强调了激励性主题，并提出了促进和加快 5G-RAN 发展的建议。本综述提供了基于学习的映射，从计算能力和资源可用性的角度描述了 5G-RAN 架构（如 O-RAN、C-RAN、HCRAN 和 F-RAN 等）的现状。此外，文章还指出了当前实施的 ML 预测概念（分类预测与价值预测），并讨论了未来在网络智能目标方面的改进领域。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Network and Computer Applications 工程技术-计算机：跨学科应用

CiteScore

21.50

自引率

3.40%

发文量

142

审稿时长

37 days

期刊介绍： The Journal of Network and Computer Applications welcomes research contributions, surveys, and notes in all areas relating to computer networks and applications thereof. Sample topics include new design techniques, interesting or novel applications, components or standards; computer networks with tools such as WWW; emerging standards for internet protocols; Wireless networks; Mobile Computing; emerging computing models such as cloud computing, grid computing; applications of networked systems for remote collaboration and telemedicine, etc. The journal is abstracted and indexed in Scopus, Engineering Index, Web of Science, Science Citation Index Expanded and INSPEC.