Artificial Intelligence for Network Resource Allocation and Spectrum Management in 5G and 6G: A Systematic Review
Abstract
The rapid growth of wireless connectivity has intensified the demand for efficient utilization of spectrum, computing resources, energy, and network infrastructure. Dynamic resource allocation is particularly challenging in 5G and emerging 6G networks because of heterogeneous users, diverse service requirements, dense deployments, and rapidly changing channel and traffic conditions. Artificial Intelligence (AI) offers adaptive mechanisms for learning complex resource-management policies and optimizing network performance. This review provides a systematic analysis of AI-based techniques for spectrum allocation, power control, channel assignment, beam management, user association, computation allocation, and radio-resource management. Supervised learning, unsupervised learning, deep learning, reinforcement learning, and multi-agent reinforcement learning approaches are categorized and compared. The review examines optimization objectives including spectral efficiency, energy efficiency, latency, fairness, reliability, and throughput. Particular attention is given to distributed intelligence and edge-based learning for reducing decision latency. Key challenges involving training complexity, non-stationary environments, scalability, interference, privacy, and model robustness are discussed. The paper identifies open research directions for AI-native resource management in future 6G networks
References
Anand, A. (2025). Self-Healing Network Infrastructure using AI-based Intent Recognition. Available at SSRN 5726222.
Satish, E. G., Mounika, B. V. S., Anand, A., Venkata, A. K. P., & Chandrashekar, G. (2025, October). Adaptive Sdn-Based Lightweight Cryptographic Framework for Post-Quantum Secure Smart Home Iot Networks. In 2025 2nd International Conference on Software, Systems and Information Technology (SSITCON) (pp. 01-08). IEEE.
Wang, M., Cui, Y., Wang, X., Xiao, S., & Jiang, J. (2018). Machine learning for networking: Workflow, advances and opportunities. IEEE Network, 32(2), 92–99. https://doi.org/10.1109/MNET.2017.1700200
Zhang, C., Patras, P., & Haddadi, H. (2019). Deep learning in mobile and wireless networking: A survey. IEEE Communications Surveys & Tutorials, 21(3), 2224–2287. https://doi.org/10.1109/COMST.2019.2904897
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
Tyagi, N. K., Singh, H., Prasad, A., Tewari, R., Anand, A., & Ranjan, P. (2025, August). PPO-RA: A Proximal Policy Optimization-Based Deep Reinforcement Learning Framework for Adaptive Resource Allocation in Cloud–Edge–Mist Environments. In 2025 2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-7). IEEE.
Shankar, S. S., MuhsnHasan, M., Anand, A., Singh, S., & Kavitha, K. R. (2025, August). Trust-Aware Fuzzy Clustering and Hippopotamus Optimization Algorithm for Secure and Energy-Efficient Routing in Wireless Sensor Networks. In 2025 2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-8). IEEE.
Anand, A., Singh, B., & Prabhat, S. (2025). Policy-Driven Automation in Cloud Backbones: A Network Slicing Approach Using AWS Cloud WAN.
