Authors : A. Abhishek; Akash J.

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/w58urszd

Scribd : https://tinyurl.com/2hz6xf88

DOI : https://doi.org/10.38124/ijisrt/26May105

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The exponential growth of fifth-generation (5G) wireless technology has generated an unprecedented volume of academic scholarship, making it essential to understand the trajectory, geography, and thematic evolution of this research body. This study presents a comprehensive bibliometric analysis of 5G-related literature indexed in the Web of Science (WoS) Core Collection from 2015 to early 2026, encompassing 8,742 publications drawn from 94 countries. Using VOSviewer, Biblioshiny (R), and CiteSpace, the study maps authorship patterns, country-level contributions, interinstitutional collaborations, and keyword co-occurrence clusters. The findings reveal a decisive shift in research focus: from foundational infrastructure questions that dominated the early literature to applied themes emphasizing artificial intelligence (AI) integration, network slicing, massive MIMO, edge computing, and the early architecture of both 5GAdvanced (Release 18 and beyond) and sixth-generation (6G) systems. China, the United States, and South Korea emerge as the most prolific contributors, accounting for collectively over 42% of total publications, while co-authorship analysis point outs the deepening North–South and cross-continental research partnerships. Keyword trend analysis confirms that AI-native network automation, energy efficiency, and ultra-reliable low-latency communications (URLLC) represent the current frontier of inquiry. By 2025, 5G had reached approximately 3 billion subscribers globally, covering around 55% of the world's population, and the AI–5G market is projected to surpass USD 4.85 billion in 2026.

Keywords : 5G Technology, Bibliometric Analysis, 5G-Advanced, 6G, Artificial Intelligence, Network Slicing, VOSviewer, Web of Science, Wireless Communications, IoT.

References :

1. Ahmed, S., Arif, O., Kaleem, Z., & Qureshi, I. M. (2019). A bibliometric analysis of 5G technology research from 2013 to 2018. IEEE Access, 7, 159618–159636. https://doi.org/10.1109/ACCESS.2019.2952109
2. Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
3. Chen, M., Miao, Y., Jian, X., Wang, X., & Humar, I. (2020). Cognitive-inspired NB-IoT smart health system with energy efficiency. Mobile Networks and Applications, 25(3), 1119–1125. https://doi.org/10.1007/s11036-019-01249-1
4. Glänzel, W., & Schubert, A. (2004). Analysing scientific networks through co-authorship. In H. F. Moed, W. Glänzel, & U. Schmoch (Eds.), Handbook of quantitative science and technology research (pp. 257–276). Springer. https://doi.org/10.1007/1-4020-2755-9_12
5. GSMA Intelligence. (2025). The mobile economy 2025. GSMA. https://www.gsma.com/solutions-and-impact/connectivity/mobile-economy/
6. IMT-2020. (2020). IMT-2020 (5G) promotion group white paper: 5G network architecture design. ITU. https://www.itu.int/en/ITU-R/study-groups/rsg5/rwp5d/imt-2020/Pages/default.aspx
7. Kaloxylos, A., Aroua, A., Berthet, A., Caire, G., Devroye, N., & Gesbert, D. (2021). Trustworthy AI-based communications in 6G: A vision and challenges. IEEE Network, 35(5), 12–19. https://doi.org/10.1109/MNET.001.2000410
8. Letaief, K. B., Chen, W., Shi, Y., Zhang, J., & Zhang, Y. A. (2019). The roadmap to 6G: AI empowered wireless networks. IEEE Communications Magazine, 57(8), 84–90. https://doi.org/10.1109/MCOM.2019.1900271
9. MarketsandMarkets. (2025). Artificial intelligence in 5G market—Global forecast to 2030. MarketsandMarkets Research Pvt. Ltd. https://www.marketsandmarkets.com/Market-Reports/ai-5g-market-225279048.html
10. Perianes-Rodriguez, A., Waltman, L., & van Eck, N. J. (2016). Constructing bibliometric networks: A comparison between full and fractional counting. Journal of Informetrics, 10(4), 1178–1195. https://doi.org/10.1016/j.joi.2016.10.006
11. Rajatheva, N., Atzeni, I., Bjornson, E., Bourdoux, A., Buzzi, S., Dore, J.-B., Erkucuk, S., Fuentes, M., Ghauch, H., Han, Y., Haas, H., Halkola, T., Hollanti, C., Hämäläinen, J., Huschke, J., Imran, M., Juntti, M., Leinonen, M. E., Leshem, A., & Wymeersch, H. (2020). White paper on broadband connectivity in 6G. arXiv preprint. https://doi.org/10.48550/arXiv.2004.14247
12. 3GPP. (2024). Release 18 description: Summary of rel-18 work items (Technical Report 21.918 v18.0.0). 3rd Generation Partnership Project. https://www.3gpp.org/release-18
13. van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
14. Wang, W., Zhang, Z., & You, X. (2023). Semantic communications: Overview, open problems, and future research directions. IEEE Communications Surveys & Tutorials, 25(3), 1656–1701. https://doi.org/10.1109/COMST.2023.3250940
15. You, X., Wang, C.-X., Huang, J., Gao, X., Zhang, Z., Wang, M., Huang, Y., Zhang, C., Jiang, Y., Wang, J., Zhu, M., Sheng, B., Wang, D., Pan, Z., Liu, P., Qin, Z., Zhang, H., Zhang, M., Hanzo, L., … Hao, W. (2021). Towards 6G wireless communication networks: Vision, enabling technologies, and new paradigm shifts. Science China Information Sciences, 64(1), 110301. https://doi.org/10.1007/s11432-020-2955-6
16. Zhang, J., Chen, S., Lin, Y., Zheng, J., Ai, B., & Hanzo, L. (2022). Cell-free massive MIMO: A new next-generation paradigm. IEEE Access, 7, 99878–99888. https://doi.org/10.1109/ACCESS.2022.3211002
17. Ziegler, V., Wild, T., Uusitalo, M. A., Flinck, H., Räisänen, V., & Hämäläinen, S. (2020). Evolution of future B5G and 6G use cases: Service requirements and KPIs. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2020), 1–7. https://doi.org/10.1109/PIMRC48278.2020.9217365