Radio access network slicing

Point of contact

Véronique Vèque, L2S, Université Paris Saclay,


In the fifth-generation communication systems, Network slicing (NS) aims at replacing the traditional “one-size-fits-all » network architecture. Thanks to its ability to provide a higher network flexibility, NS may address diverging requirements imposed by verticals while providing a reduction of operational costs. NS exploits network virtualization to elastically allocate and reallocate infrastructure resources tailored to time-varying needs of various applications. With NS, multiple slices, i.e., customized, isolated, and service-dedicated end-to-end logical networks can be established and operated simultaneously on a common physical infrastructure network.

Network slicing opens many research directions such as the resource provisioning problem to provide slices with guaranteed quality of service, the optimal share of virtualization between the radio heads and the access network, the slice resource alocation accounting for uncertainties related to the user number, demand, locations… MILP are often encoutered in this context, and efficient heuristics have to be found.


Network function virtualization, RAN virtualization, Slicing, MILP, Reinforcement learning

Researchers involved or interested

A few references

  • J. Ordonez-Lucena, P. Ameigeiras, D. Lopez, J. J. Ramos-Munoz, J. Lorca, and J. Folgueira, “Network Slicing for 5G with SDN/NFV: Concepts, Architectures, and Challenges,” IEEE Communications Magazine, vol. 55, no. 5, pp. 80–87, 2017.
  • C. Liang and F. R. Yu, “Wireless Network Virtualization: A Survey, Some Research Issues and Challenges,” IEEE Commun. Surveys Tuts., pp. 1–24, 2014.
  • R. Su, D. Zhang, R. Venkatesan, Z. Gong, C. Li, F. Ding, F. Jiang, and Z. Zhu, “Resource Allocation for Network Slicing in 5G Telecommunication Networks: A Survey of Principles and Models,” IEEE Network, vol. 33, no. 6, pp. 172–179, 2019.
  • 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, 2020.
  • D. Bega, M. Gramaglia, A. Banchs, V. Sciancalepore, and X. Costa-Perez, “A Machine Learning Approach to 5G Infrastructure Market Optimization,” IEEE Trans. Mobile Comput., vol. 19, no. 3, pp. 498–512, 2020