AI-Native 6G Networks: A Comprehensive Review of Machine Learning, Edge Intelligence, and Autonomous Network Architectures
Abstract
The evolution toward sixth-generation (6G) wireless networks is expected to introduce highly heterogeneous infrastructures involving artificial intelligence, edge computing, integrated sensing and communication, intelligent surfaces, and extremely dynamic network environments. AI is increasingly regarded not merely as an application but as a fundamental component of future network architecture. This review examines the role of Artificial Intelligence in the development of AI-native 6G networks. It covers AI-enabled radio resource management, intelligent beamforming, spectrum management, mobility management, network slicing, edge intelligence, semantic communication, and autonomous network orchestration. Machine learning, deep learning, reinforcement learning, federated learning, and graph-based learning techniques are compared across different networking scenarios. The paper further examines the relationship between distributed AI and edge computing, emphasizing latency, energy efficiency, privacy, and communication overhead. Key challenges involving scalability, trustworthy AI, model distribution, adversarial threats, interoperability, and standardization are discussed. The review concludes by identifying important research directions for developing fully autonomous, intelligent, and sustainable 6G networks.
References
Anand, A., Singh, B., & Prabhat, S. (2025). Policy-Driven Automation in Cloud Backbones: A Network Slicing Approach Using AWS Cloud WAN.
Mao, Q., Hu, F., & Hao, Q. (2018). Deep learning for intelligent wireless networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 20(4), 2595–2621. https://doi.org/10.1109/COMST.2018.2846401
Mao, B., Tang, F., Kawamoto, Y., & Kato, N. (2021). Optimizing computation offloading in satellite-terrestrial networks through supermodular game. IEEE Wireless Communications, 28(5), 18–25.
Luong, N. C., Hoang, D. T., Gong, S., Niyato, D., Wang, P., Liang, Y.-C., & Kim, D. I. (2019). Applications of deep reinforcement learning in communications and networking: A survey. IEEE Communications Surveys & Tutorials, 21(4), 3133–3174. https://doi.org/10.1109/COMST.2019.2916583
Tang, F., Mao, B., Kawamoto, Y., & Kato, N. (2021). Survey on machine learning for intelligent end-to-end communication toward 6G: From network access, routing to traffic control and streaming adaption. IEEE Communications Surveys & Tutorials, 23(3), 1578–1598. https://doi.org/10.1109/COMST.2021.3073009
Letaief, K. B., Shi, Y., Lu, J., & Lu, J. (2019). The roadmap to 6G: AI empowered wireless networks. IEEE Communications Magazine, 57(8), 84–90. https://doi.org/10.1109/MCOM.2019.1900271
Saad, W., Bennis, M., & Chen, M. (2020). A vision of 6G wireless systems: Applications, trends, technologies, and open research problems. IEEE Network, 34(3), 134–142. https://doi.org/10.1109/MNET.001.1900287
Zhang, Z., Xiao, Y., Ma, Z., Xiao, M., Ding, Z., Lei, X., Karagiannidis, G. K., & Fan, P. (2019). 6G wireless networks: Vision, requirements, architecture, and key technologies. IEEE Vehicular Technology Magazine, 14(3), 28–41. https://doi.org/10.1109/MVT.2019.2921477
Dang, S., Amin, O., Shihada, B., & Alouini, M.-S. (2020). What should 6G be? Nature Electronics, 3, 20–29. https://doi.org/10.1038/s41928-019-0355-6
Akyildiz, I. F., Kak, A., & Nie, S. (2020). Internet of Things (IoT): 5G and 6G vision, architecture, and applications. Microprocessors and Microsystems, 77, 103237. https://doi.org/10.1016/j.micpro.2020.103237
Shafin, R., Chen, L., Matinmikko-Blue, M., & Saad, W. (2020). Artificial intelligence-enabled cellular networks: A survey. IEEE Communications Surveys & Tutorials, 22(1), 795–819.
Sirohi, D., Kumar, N., Rana, P. S., Tanwar, S., Iqbal, R., & Hijjii, M. (2023). Federated learning for 6G-enabled secure communication systems: A comprehensive survey. Artificial Intelligence Review, 56, 1–93. https://doi.org/10.1007/s10462-023-10417-3
Niknam, S., Dhillon, H. S., & Reed, J. H. (2020). Federated learning for wireless communications: Motivation, opportunities and challenges. IEEE Communications Magazine, 58(6), 46–51.
Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., & Miao, C. (2020). Federated learning in mobile edge networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 22(3), 2031–2063.
Anand, A. (2025). Self-Healing Network Infrastructure using AI-based Intent Recognition. Available at SSRN 5726222.
