Article Open Access

Beyond 5G: Exploring AI-Driven Network Optimisation for 6G Communications

Kunal Meher, S. Karthikeyan, Bharat Jyoti Ranjan Sahu, M.P. Sunil, Smita Mishra, Amanveer Singh, Kukatla Tejesh

Abstract


This research consists of various features of 5G networks; the vision for 6G networks promises significant advancements, including ultra-high data rates, sub-millisecond latency, highly intelligent network operations, and exceptional device interconnectivity, among others.  Artificial Intelligence (AI) meets these requirements, which act as a fundamental base in self-organising and proactive adaptive network management. In the scope of this paper, AI integration with core 6G network functions is considered, including AI techniques such as machine learning, deep learning, federated learning, and reinforcement learning. Focus is on the AI-driven optimisation of spectrum utilisation, user experience, traffic pattern prediction, dynamic network slicing, robust QoS, and responsive QoS retention. Advancing edge computing, reconfigurable intelligent surfaces (RIS), and digital twins are also discussed. The study also discusses the lack of AI governance in 6G infrastructure, which includes data privacy, transparency of the algorithms, energy expenses, and global standardisation. This research focus reveals the highlights of the primary gaps in design and governance rationale that emerge through the lack of AI-integrated structural frameworks, resigns through the absence of a designed fabric needed to supplant the transcending potential of 6G enabled autonomous communication systems AI will irrevocably purge and define the naivety behind detonating the boundless potential AI entrenched paradigms will deliver.


Keywords


6G Communication, Network Optimisation, Machine Learning, Deep Learning, Edge Intelligence

References


Niknam, S., Li, H., Yang, J., & Seneviratne, A. (2020). Federated learning for wireless communications: Motivation, opportunities, and challenges. IEEE Communications Magazine, 58(6), 46–51. https://doi.org/10.1109/MCOM.001.1900108

Mayilsamy, J., & Rangasamy, D. P. (2021). Enhanced Routing Schedule - Imbalanced Classification Algorithm for IOT based Software Defined Networks. International Academic Journal of Science and Engineering, 8(1), 01–09. https://doi.org/10.9756/IAJSE/V8I1/IAJSE0801

Simsek, M., Akyildiz, I. F., & Fettweis, G. P. (2019). 6G-enabled wireless networks: Opportunities, challenges, and research directions. EURASIP Journal on Wireless Communications and Networking, 2019(1), 1–13. https://doi.org/10.1186/s13638-019-1507-2.

Muralidharan, J. (2024). Advancements in 5G technology: Challenges and opportunities in communication networks. Progress in Electronics and Communication Engineering, 1(1), 1–6. https://doi.org/10.31838/PECE/01.01.01

Saidova, K., Abdullayeva, S., Yakubova, D., Gudalov, M., Abdurahmonova, K., Khudoykulova, H., Mukhammadova, G., & Zokirov, K. (2024). Assessing the Economic Benefits of Climate Change Mitigation and Adoption Strategies for Aquatic Ecosystem. International Journal of Aquatic Research and Environmental Studies, 4(S1), 20-26. https://doi.org/10.70102/IJARES/V4S1/4

Atia, M. (2025). Breakthroughs in tissue engineering techniques. Innovative Reviews in Engineering and Science, 2(1), 1-12. https://doi.org/10.31838/INES/02.01.01

Tang, W., Chen, M. Z., Zeng, Y., Yuen, C., Zhang, S., & Jin, Y. (2021). Wireless communications with reconfigurable intelligent surface: Path loss modeling and experimental measurement. IEEE Transactions on Wireless Communications, 20(1), 421–439. https://doi.org/10.1109/TWC.2020.3023305

Thang, N. C., & Park, M. (2020). Detecting Malicious Middleboxes in Service Function Chaining. Journal of Internet Services and Information Security, 10(2), 82-90.

Ibrahim, M. S., & Shanmugaraja, P. (2023). Mobility Based Routing Protocol Performance Oriented Comparative Analysis in the ADHOC Networks FANET, MANET and VANET using OPNET Modeler for FTP and Web Applications. International Academic Journal of Innovative Research, 10(1), 14–24. https://doi.org/10.9756/IAJIR/V10I1/IAJIR1003.

Jakhir, C., Rudevdagva, R., & Riunaa, L. (2023). Advancements in the novel reconfigurable Yagi antenna. National Journal of Antennas and Propagation, 5(1), 33–38.

Muralidharan, J. (2024). Optimization techniques for energy-efficient RF power amplifiers in wireless communication systems. SCCTS Journal of Embedded Systems Design and Applications, 1(1), 1-6. https://doi.org/10.31838/ESA/01.01.01

Zhang, C., Patras, P., & Haddadi, H. (2020). Deep learning in mobile and wireless networking: A survey. IEEE Communications Surveys & Tutorials, 21(3), 2224–2287. https://doi.org/10.1109/COMST.2019.2916588

Rojas, C., & García, F. (2024). Optimizing Traffic Flow in Smart Cities: A Simulation-based Approach Using IoT and AI Integration. Association Journal of Interdisciplinary Technics in Engineering Mechanics, 2(1), 19-22.

Shuen, T. K., Talib, C. A., Osman, S., Ying, S. T., Ahmad, I. S., Anggoro, S., Erna, M., & Fah, L. Y. (2024). Integrated Framework for the Implementation of Visual Programming Language in Science Experiment for Secondary School. Indian Journal of Information Sources and Services, 14(3), 45–51. https://doi.org/10.51983/ijiss-2024.14.3.07

Sathish Kumar, T. M. (2023). Wearable sensors for flexible health monitoring and IoT. National Journal of RF Engineering and Wireless Communication, 1(1), 10-22. https://doi.org/10.31838/RFMW/01.01.02




DOI: https://doi.org/10.52088/ijesty.v5i1.1305

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Copyright (c) 2025 Kunal Meher, S. Karthikeyan, Bharat Jyoti Ranjan Sahu, M.P. Sunil, Smita Mishra, Amanveer Singh, Kukatla Tejesh

International Journal of Engineering, Science, and Information Technology (IJESTY) eISSN 2775-2674