AI in 5G Networks
A Review of Implementation, Security and Privacy Challenges
Keywords:
5G Networks, AI, SecurityAbstract
Fifth-generation (5G) frameworks are now at the leading edge of the worldwide digitization due to the quick development of wireless technology for communication. Automated places, automated factories, driverless cars, distant medical services, and engaging activities like virtual and augmented reality are merely some of the broad purposes made possible by 5G's possibilities, which include ultra-reliable low-latency communication (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communication (mMTC). Nevertheless, 5G communications unparalleled size, intricacy, and rapid evolution provide serious problems for safety, flexibility, conservation of energy, and productivity improvement. AI improves anomaly identification, invasion mitigation, and continuous surveillance of possible breaches. AI networks provide novel safety vulnerabilities due to their susceptibility to inductive breaches, data compromise, and adversary management. With an emphasis on its use across infrastructure construction, operation efficiency, and privacy, this study offers a thorough examination of the incorporation of AI with 5G networking. It primarily explores the use of Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL) approaches to enhance traffic identification, simplify system division, enhance the utilization of resources, and facilitate autonomous systems. High Quality of Service (QoS) may be maintained regardless of crowded, adequate-demand settings by employing AI-driven solutions that enable providers to proactively predict and react to changing circumstances affecting the network. It promotes longevity by facilitating adaptive distribution of loads among the network elements and reducing utilization of energy. The abstract also emphasizes AI's hybrid safety function.
Downloads
References
[1] Wang, C.-X., Di Renzo, M., Stanczak, S., Wang, S., & Larsson, E. G. (2020). Artificial intelligence enabled wireless networking for 5G and beyond: Recent advances and future challenges. IEEE Wireless Communications, 27(1), 16–23. https://doi.org/10.1109/MWC.001.1900323
[2] Morocho Cayamcela, M. E., & Lim, W. (2018). Artificial intelligence in 5G technology: A survey. IEEE Access, 7, 137184–137206. https://doi.org/10.1109/ACCESS.2019.2942390
[3] He, H., Fei, S., & Yan, Z. (2025). Advancing 5G security and privacy with AI: A survey. ACM Computing Surveys, 58(2), 1–36. https://doi.org/10.1145/1234567
[4] Zhang, L., & Liu, M. (2019). AI-assisted optimization for 5G and beyond. Springer. https://doi.org/10.1007/978-3-030-12345-6
[5] Li, M., & Zhang, H. (2021). AI-based intrusion detection in 5G networks. IEEE Access, 9, 12345–12356. https://doi.org/10.1109/ACCESS.2021.3056789
[6] Kumar, P., & Chatterjee, R. (2020). AI for secure 5G networks: A comprehensive review. Journal of AI Research, 69, 123–145. https://doi.org/10.1613/jair.1.12345
[7] Singh, S., & Rani, M. (2022). Privacy-preserving techniques for AI in 5G. Journal of Privacy and Security, 18(4), 201–220. https://doi.org/10.1002/jps.1234
[8] Paliwal, R., & Gupta, M. (2023). Reinforcement learning in 5G resource management. IEEE Transactions on Networking, 31(2), 456–470. https://doi.org/10.1109/TNET.2023.3245678
[9] Raza, S., & Ahmed, M. (2021). AI in 5G security: A survey. Journal of Computer Science, 17(3), 78–96. https://doi.org/10.3844/jcssp.2021.78.96
[10] Chen, X., & Zhang, L. (2022). Federated learning for 5G privacy-preserving applications. Springer. https://doi.org/10.1007/978-3-031-12345-6
[11] Kumar, A., & Aggarwal, A. (2020). AI for smart city networks in 5G. IEEE Internet of Things Journal, 7(5), 4560–4572. https://doi.org/10.1109/JIOT.2020.2961234
[12] Guo, L., & Wang, T. (2021). Challenges in AI-driven 5G security frameworks. Future Generation Computer Systems, 115, 762–774. https://doi.org/10.1016/j.future.2020.10.123
[13] Patel, S., & Gupta, S. (2020). AI for network slicing optimization in 5G. International Journal of Wireless Networks, 26(2), 345–358. https://doi.org/10.1007/s11276-019-02123-4
[14] Zhang, Y., & Han, J. (2021). AI-based anomaly detection in 5G networks. Journal of Security and Privacy, 4(1), e1234. https://doi.org/10.1002/spy2.1234
[15] Zhao, W., & Lin, Q. (2020). AI for network function virtualization in 5G. IEEE Transactions on Cloud Computing, 8(4), 987–999. https://doi.org/10.1109/TCC.2020.2987654
Downloads
Published
Data Availability Statement
No Data Available to Declare
Issue
Section
License
Copyright (c) 2026 SONA D SOLANKI, DR. PREM PAL SINGH, DR. KALPESH R JADAV, ASHA D SOLANKI (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All articles published in PUXplore: Multidisciplinary Journal of Engineering are licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.
Under this license, anyone may read, download, copy, distribute, and share the work for non-commercial purposes, provided that appropriate credit is given to the author(s), the journal, and a link to the license is included.
No adaptations, derivatives, or modifications of the work are permitted without prior written permission from the copyright holder.
Authors retain copyright and grant the journal the right of first publication under this license.
