AI-Enabled Energy-Efficient Network Slicing and Resource Scheduling in 5G-Advanced Wireless Systems
Keywords:
5G-Advanced, network slicing, energy efficiency, resource scheduling, artificial intelligence, deep reinforcement learning, quality of service, sustainability, system governanceAbstract
The transition to 5G-Advanced and beyond represents a fundamental shift in wireless system design, demanding infrastructures that simultaneously support heterogeneous service requirements, dynamic resource allocation, and stringent energy efficiency targets. Network slicing has emerged as a cornerstone architectural principle, enabling multiple logical networks to coexist on a shared physical substrate. However, the operational complexity of orchestrating slices with diverse quality-of-service profiles under time-varying traffic and channel conditions pushes traditional heuristic and optimization-based resource scheduling to their limits. Artificial intelligence, particularly deep reinforcement learning, offers a transformative pathway toward adaptive, self-optimizing slice management that can jointly pursue energy conservation and performance guarantees. This paper provides a comprehensive systems-level analysis of AI-enabled energy-efficient network slicing and resource scheduling in 5G-Advanced wireless systems. It examines the architectural enablers, the integration of machine learning into slice orchestration, and the structural trade-offs between efficiency, fairness, robustness, and sustainability. Discussions span from centralized and distributed learning paradigms to model interpretability, deployment feasibility, and cross-domain governance. The analysis emphasizes infrastructure-scale implications, including life-cycle energy impacts, resource isolation, and the alignment of AI-driven decisions with regulatory and standardization frameworks. By synthesizing perspectives from telecommunications, systems engineering, and socio-technical governance, the paper articulates a forward-looking research agenda that prioritizes not only algorithmic innovation but also systemic resilience, policy coherence, and long-term environmental accountability in next-generation networks.
References
1. M. Shafi, A. F. Molisch, P. J. Smith, T. Haustein, P. Zhu, P. De Silva, F. Tufvesson, A. Benjebbour, S. Parkvall, and T. Wild, "5G: A Tutorial Overview of Standards, Trials, Challenges, Deployment, and Practice," IEEE Journal on Selected Areas in Communications, vol. 35, no. 6, pp. 1201-1221, 2017.
2. X. Foukas, G. Patounas, A. Elmokashfi, and M. K. Marina, "Network Slicing in 5G: Survey and Challenges," IEEE Communications Magazine, vol. 55, no. 5, pp. 94-100, 2017.
3. Q. Wu, G. Y. Li, W. Chen, D. W. K. Ng, and R. Schober, "An Overview of Sustainable Green 5G Networks," IEEE Wireless Communications, vol. 24, no. 4, pp. 72-80, 2017.
4. M. Chen, U. Challita, W. Saad, C. Yin, and M. Debbah, "Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial," IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3039-3071, 2019.
5. N. C. Luong, D. T. Hoang, S. Gong, D. Niyato, P. Wang, Y. C. Liang, and D. I. Kim, "Applications of Deep Reinforcement Learning in Communications and Networking: A Survey," IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3133-3174, 2019.
6. H. Zhang, N. Liu, X. Chu, K. Long, A. H. Aghvami, and V. C. M. Leung, "Network Slicing Based 5G and Future Mobile Networks: Mobility, Resource Management, and Challenges," IEEE Communications Magazine, vol. 55, no. 8, pp. 138-145, 2017.
7. Li, Q. (2026). QoS Assurance Mechanism for 5G Network Slicing Based on the Deep Reinforcement Learning PPO Algorithm. arXiv preprint arXiv:2605.03345.
8. A. N. Al-Quzweeni, A. Q. Lawey, T. E. El-Gorashi, and J. M. H. Elmirghani, "Energy Efficient Resource Allocation in 5G Network Slicing," IEEE Transactions on Green Communications and Networking, vol. 3, no. 3, pp. 681-698, 2019.
9. J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, "Proximal Policy Optimization Algorithms," arXiv preprint arXiv:1707.06347, 2017.
10. S. Guo, T. Xing, and H. Li, "Fairness-Aware Resource Allocation in Network Slicing for 5G Systems," IEEE Access, vol. 8, pp. 102132-102145, 2020.
11. W. Samek, G. Montavon, S. Lapuschkin, C. J. Anders, and K.-R. Müller, "Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications," Proceedings of the IEEE, vol. 109, no. 3, pp. 247-278, 2021.
12. C. Tselios and G. Tsolis, "On Standardization of 5G Network Slicing: A Review of Current Activities and Challenges," IEEE Communications Standards Magazine, vol. 4, no. 2, pp. 38-45, 2020.
13. L. Belkhir and A. Elmeligi, "Assessing ICT Global Emissions Footprint: Trends to 2040 & Recommendations," Journal of Cleaner Production, vol. 177, pp. 448-463, 2018.
14. J. Park, M. Mahmoudi, and J. Lee, "AI/ML-Based Services and Optimization in 5G-Advanced," IEEE Journal on Selected Areas in Communications, vol. 40, no. 6, pp. 1835-1841, 2022.
15. R. Mijumbi, J. Serrat, J. L. Gorricho, N. Bouten, F. De Turck, and R. Boutaba, "Network Function Virtualization: State-of-the-Art and Research Challenges," IEEE Communications Surveys & Tutorials, vol. 18, no. 1, pp. 236-262, 2016.
16. S. Buzzi, C. L. I, T. E. Klein, H. V. Poor, C. Yang, and A. Zappone, "A Survey of Energy-Efficient Techniques for 5G Networks and Challenges Ahead," IEEE Journal on Selected Areas in Communications, vol. 34, no. 4, pp. 697-709, 2016.
17. Z. Zhou, X. Chen, E. Li, L. Zeng, K. Luo, and J. Zhang, "Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing," Proceedings of the IEEE, vol. 107, no. 8, pp. 1738-1762, 2019.
18. P. Rost, C. Mannweiler, D. S. Michalopoulos, C. Sartori, V. Sciancalepore, N. Sastry, O. Holland, S. Tayade, B. Han, D. Bega, and R. Vilalta, "Network Slicing to Enable Scalability and Flexibility in 5G Mobile Networks," IEEE Communications Magazine, vol. 55, no. 5, pp. 72-79, 2017.
19. M. Yan, C. A. Chan, A. F. Gygax, J. Yan, L. Campbell, A. Nirmalathas, and C. Leckie, "Making Telecommunications Networks Greener: State-of-the-Art and Future Research Directions," IEEE Communications Surveys & Tutorials, vol. 23, no. 2, pp. 1268-1304, 2021.
20. A. Kammoun, N. Tabbane, G. R. Blakenship, M. Debbah, and R. S. Adve, "Quality of Service Provisioning in 5G Networks: A Comprehensive Survey and Future Directions," IEEE Open Journal of the Communications Society, vol. 2, pp. 1694-1734, 2021.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Artificial Intelligence Engineering and Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
