A survey of applications of artificial intelligence and machine learning in future mobile networks-enabled systems


Yazıcı İ., Shayea I. A. M., Din J.

Engineering Science and Technology, an International Journal, vol.44, 2023 (SCI-Expanded) identifier

  • Publication Type: Article / Review
  • Volume: 44
  • Publication Date: 2023
  • Doi Number: 10.1016/j.jestch.2023.101455
  • Journal Name: Engineering Science and Technology, an International Journal
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, INSPEC, Directory of Open Access Journals
  • Keywords: 5G, 6G, Cyber security, Deep learning, Digital twin, Intelligent transportation systems, Reinforcement learning, Smart energy, Smart healthcare, Supervised learning, Unmanned Aerial Vehicle (UAV), Unsupervised learning
  • Istanbul Technical University Affiliated: Yes

Abstract

Different fields have been thriving with the advents in mobile communication systems in recent years. These fields reap benefits of data collected by Internet of Things (IoT) in next generation (5G and 5BG) mobile networks. The IoT concept transforms different fields by providing large amount of data to be used in their operations. This is achieved by massively utilized sensors and mobile devices that acquire data from internet connected devices to keep track of physical systems. Hence, different use cases benefit from the data generated thanks to future mobile network systems. Intelligent Transportation Systems, Smart Energy, Digital Twins, Unmanned Aerial Vehicles (UAVs), Smart Health, Cyber Security are of significant use cases that big data plays an important role for them. Large amount of data entails more intelligent systems with respect to conventional methods, and it also entails highly reduced response time for use cases. Artificial intelligence and machine learning models are adept in satisfying the requirements of this big data situations for different use cases. In this sense, this paper provides a survey of machine learning and artificial intelligence applications for different use cases enabled by future mobile communication systems. An overview of machine learning types and artificial intelligence is presented to provide insights into the intelligent method concepts. Available studies are extensively summarized, and they are also grouped to provide a complete overview of the study. Discussions on the reviewed papers based on artificial intelligence and machine learning concepts are made, and some descriptive figures about the results of the discussions are also given in the paper. Finally, research challenges for artificial intelligence and machine learning applications in the use cases are introduced, future research directions and concluding remarks are presented accordingly.