Mapping Deep Learning Technologies for Mobile Networks with the Internet of Things

Deep learning technologies for mobile networks have been regarded as a popular issue due to the development of mobile technology, deep learning, and mobile smart applications. This paper investigates research trends in deep learning technologies for mobile networks in computer science and technology research by analyzing the articles published from 2016 to 2022 in the journals included in the Web of Science (WoS) database. That is, SSCI/SCI journal articles were collected to analyze the most frequently cited relevant articles, countries, authors, institutions, and application areas, and to investigate the international research trends of deep learning technologies for mobile networks via VOSviewer. The analysis results identified four main collaborating institutions, namely King Saud University, Beijing University of Post & Telecommunications, the University of Electronic Science & Technology of China, and Xidian University, as well as four keyword clusters, covering the Internet of Things, edge computing, and deep learning. The results also show that the study of Li, Ota, and Dong's (2018) is the most frequently cited article in the field of deep learning technologies for mobile networks. The main application areas are Engineering Electrical Electronic, followed by Computer Science Information Systems and Telecommunications. The results of this study highlight the international research trend of deep learning technologies for mobile networks, and provide new insights for researchers.