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

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.