Deep reinforcement learning for dynamic network slicing in IEEE 802.11 networks

Sibren De Bast, Rodolfo Torrea-Duran, Alessandro Chiumento, Sofie Pollin, Haris Gacanin

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

22 Citations (Scopus)


Network slicing, a key enabler for future wireless networks, divides a physical network into multiple logical networks that can be dynamically created and configured. In current IEEE 802.11 (Wi-Fi) networks, the only form of network configuration is a rule-based optimization of few parameters. Future access points (APs) are expected to have self-organizational capabilities, able to deal with large configuration spaces in order to dynamically configure each slice. Deep Reinforcement Learning (DRL) can achieve promising results in highly dynamic and complex environments without the need for an operating model, by learning the optimal strategy after interacting with the environment. However, since the number of possible slice configurations is huge, achieving the optimal strategy requires an exhaustive learning period that might yield an outdated slice configuration. In this paper, we propose a fast-learning DRL model that can dynamically optimize the slice configuration of unplanned Wi-Fi networks without expert knowledge. Enhanced with an off-line learning step, the proposed approach is able to achieve the optimal slice configuration with a fast convergence, which is attractive for dynamic scenarios.
Original languageEnglish
Title of host publicationIEEE INFOCOM 2019-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
Number of pages6
ISBN (Electronic)978-1-7281-1878-9
Publication statusPublished - 2019
Externally publishedYes
EventIEEE International Conference on Computer Communications, INFOCOM 2019 - Paris, France
Duration: 29 Apr 20192 May 2019


ConferenceIEEE International Conference on Computer Communications, INFOCOM 2019
Abbreviated titleINFOCOM 2019


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