AI for 5G networking- A Bibliometric Analysis

Authors

  • Smita Mahajan Artificial Intelligence and Machine Learning Department, Symbiosis Institute of Technology, (Pune campus), Lavale, Pune-412115, Maharashtra, India.
  • Shivali Amit Wagle Artificial Intelligence and Machine Learning Department, Symbiosis Institute of Technology, (Pune campus), Lavale, Pune-412115, Maharashtra, India.
  • Sunil M. Sangve Department of Artificial Intelligence and Data Science, Vishwakarma Institute of Technology, Pune-411037, Maharashtra, India
  • Aryani Gangadhara JSPMs Rajarshi Shahu College of Engineering, Tathawade, Pune-411033, Maharashtra, India

Keywords:

bibliometric analysis, 5G network, data analysis, artificial intelligence

Abstract

The merging of 5G networking with artificial intelligence (AI) has become a game-changing paradigm, completely overwhelming the landscape of communication systems. A thorough bibliometric analysis is presented in this work to help comprehend the developments, patterns, and significant research areas at the nexus of artificial intelligence (AI) and fifth-generation(5G). Through an extensive assessment of conference papers, patents, and academic literature, this investigation delves into the complex interactions that arise between artificial intelligence and 5G technology. This bibliometric analysis is a valuable tool for scholars, policymakers, and industry experts trying to understand the intricacies of AI-enabled 5G networks, as it offers a broad perspective of the scholarly scene. In this dynamic and quickly developing sector, it provides insights into the current level of knowledge, points out research gaps, and lays-out a plan for future studies.

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Published

23.02.2024

How to Cite

Mahajan, S. ., Wagle, S. A. ., Sangve, S. M. ., & Gangadhara, A. . (2024). AI for 5G networking- A Bibliometric Analysis. International Journal of Intelligent Systems and Applications in Engineering, 12(17s), 28–39. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4834

Issue

Vol. 12 No. 17s (2024)

Section

Research Article

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