Reinforcement Learning for Slicing in a 5G Flexible RAN
Journal article, 2019

Network slicing enables an infrastructure provider (InP) to support heterogeneous 5G services over a common platform (i.e., by creating a customized slice for each service). Once in operation, slices can be dynamically scaled up/down to match the variation of their service requirements. An InP generates revenue by accepting a slice request. If a slice cannot be scaled up when required, an InP has to also pay a penalty (proportional to the level of service degradation). It becomes then crucial for an InP to decide which slice requests should be accepted/rejected in order to increase its net profit.  This paper presents a slice admission strategy based on reinforcement learning (RL) in the presence of services with different priorities. The use case considered is a 5G flexible radio access network (RAN), where slices of different mobile service providers are virtualized over the same RAN infrastructure. The proposed policy learns which are the services with the potential to bring high profit (i.e., high revenue with low degradation penalty), and hence should be accepted. The performance of the RL-based admission policy is compared against two deterministic heuristics. Results show that in the considered scenario, the proposed strategy outperforms the benchmark heuristics by at least 55%. Moreover, this paper shows how the policy is able to adapt to different conditions in terms of: (i)slice degradation penalty vs. slice revenue factors, and (ii)proportion of high vs. low priority services.

software defined networking (SDN)

5G

flexible RAN

cloud RAN

slice admission control

optical networks

network function virtualization (NFV)

dynamic slicing

reinforcement learning

Author

Muhammad Rehan Raza

Ericsson AB

Carlos Natalino Da Silva

Chalmers, Electrical Engineering, Communication and Antenna Systems, Optical Networks

Peter Öhlen

Ericsson Research

Lena Wosinska

Chalmers, Electrical Engineering, Communication and Antenna Systems, Optical Networks

Paolo Monti

Chalmers, Electrical Engineering, Communication and Antenna Systems, Optical Networks

Journal of Lightwave Technology

0733-8724 (ISSN)

Areas of Advance

Information and Communication Technology

Subject Categories

Telecommunications

Infrastructure

C3SE (Chalmers Centre for Computational Science and Engineering)

DOI

10.1109/JLT.2019.2924345

More information

Latest update

7/19/2019