Adaptive Network Slicing and Resource Allocation in 5G-Advanced Systems
DOI:
https://doi.org/10.66280/cis.v1i1.105Abstract
5G-Advanced networks are expected to support heterogeneous service classes with sharply different reliability, latency, and bandwidth requirements while operating under time-varying radio and transport constraints. This paper presents an adaptive network slicing and re- source allocation framework for 5G-Advanced systems that combines short-horizon demand prediction, slice-aware policy optimization, and hierarchical radio–compute scheduling. The proposed method, termed Hierarchical Adaptive Network Slicing Allocator (HANSA), mod- els the network slicing problem as a constrained Markov decision process in which a slice or- chestrator allocates spectrum, physical resource blocks, and edge-compute quotas to enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC) slices. A realistic experimental design is constructed using synthetic-yet-plausible traces derived from urban mobility, industrial sensing, and mixed enterprise traffic patterns over 24 weeks. The evaluation compares HANSA against static par- titioning, proportional-fair slicing, deep Q-learning, and proximal policy optimization baselines. Across all scenarios, HANSA increases aggregate throughput by 14.8% relative to the strongest baseline, reduces the 95th-percentile latency of URLLC flows by 28.6%, and improves slice satis- faction ratio from 91.7% to 96.4%. An ablation study shows that hierarchical control, predictive demand estimation, and SLA-aware reward shaping each contribute materially to the final gain. The results indicate that adaptive cross-layer slicing is a practical mechanism for translating 5G-Advanced flexibility into measurable quality-of-service improvements without excessive com- putational overhead.
References
[1] 3GPP, “Study on architecture for next generation system enhancements,” Technical Report TR 23.700-97, 2025.
[2] O-RAN Alliance, “AI-enabled near-real-time RAN intelligent controller: use cases and archi- tecture considerations,” White Paper, 2025.
[3] N. Zhang, Y. Xiao, and M. Tao, “Deep reinforcement learning for network slicing orchestration: opportunities and challenges,” IEEE Network, vol. 39, no. 2, pp. 44–52, 2025.
[4] L. Ferranti and P. Rost, “Joint radio and edge resource management for beyond-5G service platforms,” IEEE Transactions on Mobile Computing, vol. 24, no. 1, pp. 112–126, 2025.
[5] 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.
[6] H. Zhang, N. Liu, X. Chu, K. Long, A. 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] O. Sallent, J. P’erez-Romero, R. Ferr’us, and R. Agust’i, “On radio access network slicing from a radio resource management perspective,” IEEE Wireless Communications, vol. 24, no. 5, pp. 166–174, 2017.
[8] K. Katsalis, N. Nikaein, E. Schiller, A. Ksentini, and T. Braun, “Network slices toward 5G communications: Slicing the LTE network,” IEEE Communications Magazine, vol. 55, no. 8, pp. 146–154, 2017.
[9] P. Caballero, A. Banchs, G. de Veciana, and X. Costa-P’erez, “Network slicing for guaran- teed rate services: Admission control and resource allocation games,” IEEE Transactions on Wireless Communications, vol. 17, no. 10, pp. 6419–6432, 2018.
[10] A. A. Barakabitze, A. Ahmad, R. Mijumbi, and A. Hines, “5G network slicing using SDN and NFV: A survey of taxonomy, architectures and future challenges,” Computer Networks, vol. 167, art. 106984, 2020.
[11] R. Li, Z. Zhao, Q. Sun, C. I, and H. Zhang, “Deep reinforcement learning for resource man- agement in network slicing,” IEEE Access, vol. 6, pp. 74429–74441, 2018.
[12] X. Zhou, R. Li, T. Chen, and H. Zhang, “Network slicing as a service: Enabling enterprises’ own software-defined cellular networks,” IEEE Communications Magazine, vol. 54, no. 7, pp. 146–153, 2016.
[13] 3GPP, “Telecommunication management; Study on management and orchestration of network slicing for next generation network,” Technical Report TR 28.801, 2018.
[14] 3GPP, “Telecommunication management; Study on management and orchestration of network slicing for next generation network,” Technical Report TR 28.803, 2017.
[15] X. Foukas, M. K. Marina, and K. Kontovasilis, “Orca: Network slicing through efficient re- source virtualization and sharing in LTE cellular networks,” in Proc. ACM MobiCom, 2016, pp. 127–140.
[16] Y. Sun, M. Peng, Y. Zhou, Y. Huang, and S. Mao, “Application of machine learning in wireless networks: Key techniques and open issues,” IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3072–3108, 2019.
[17] Y. Wang, G. Yu, H. Zhang, P. Di Lorenzo, and M. Song, “Deep learning for wireless physical layer: Opportunities and challenges,” China Communications, vol. 17, no. 3, pp. 92–111, 2020.
[18] J. Park, S. Samarakoon, M. Bennis, and M. Debbah, “Wireless network intelligence at the edge,” Proceedings of the IEEE, vol. 107, no. 11, pp. 2204–2239, 2019.
[19] A. Thantharate, R. Paropkari, V. Walunj, and C. Beard, “DeepSlice: A deep learning approach towards an efficient and reliable network slicing in 5G communications,” in Proc. IEEE ICC, 2019, pp. 1–6.
[20] W. Jiang, G. Feng, S. Qin, and Y. Liang, “Multi-dimensional resource allocation for network slicing in 5G wireless networks,” IEEE Communications Magazine, vol. 59, no. 10, pp. 58–64, 2021.
[21] P. Rost, C. Mannweiler, D. S. Michalopoulos, C. Sartori, V. Sciancalepore, N. Sastry, and G. Bicz’ok, “Network slicing to enable scalability and flexibility in 5G mobile networks,” IEEE Communications Magazine, vol. 55, no. 5, pp. 72–79, 2017.
[22] R. Ferr’us, O. Sallent, J. P’erez-Romero, and R. Agust’i, “On 5G radio access network slicing: Radio interface protocol features and configuration,” IEEE Communications Magazine, vol. 56, no. 5, pp. 184–192, 2018.
[23] A. Kaloxylos, “A survey and an analysis of network slicing in 5G networks,” IEEE Communi- cations Standards Magazine, vol. 2, no. 1, pp. 60–65, 2018.
[24] A. B. Saleh, S. Redana, B. Raaf, J. H"am"al"ainen, and C. R. Rosa, “Performance of cloud- RAN and MEC based network slicing for 5G systems,” in Proc. European Wireless, 2019, pp. 1–6.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Computational Intelligence 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.
