Towards Automation and Augmentation of the Design of Schedulers for Cellular Communications Networks.

Autor: Fenton M; Data Science & Machine Learning Group, Corvil Ltd., Dublin, Ireland michaelfenton1@gmail.com., Lynch D; Natural Computing Research and Applications Group, School of Business, University College Dublin, Ireland david.lynch.1@ucdconnect.ie., Fagan D; Natural Computing Research and Applications Group, School of Business, University College Dublin, Ireland david.fagan@ucd.ie., Kucera S; Bell Laboratories, Nokia, Dublin, Ireland stepan.kucera@nokia-bell-labs.com., Claussen H; Bell Laboratories, Nokia, Dublin, Ireland holger.claussen@nokia-bell-labs.com., O'Neill M; Natural Computing Research and Applications Group, School of Business, University College Dublin, Ireland m.oneill@ucd.ie.
Jazyk: angličtina
Zdroj: Evolutionary computation [Evol Comput] 2019 Summer; Vol. 27 (2), pp. 345-375. Date of Electronic Publication: 2018 Mar 12.
DOI: 10.1162/evco_a_00221
Abstrakt: Evolutionary computation is used to automatically evolve small cell schedulers on a realistic simulation of a 4G-LTE heterogeneous cellular network. Evolved schedulers are then further augmented by human design to improve robustness. Extensive analysis of evolved solutions and their performance across a wide range of metrics reveals evolution has uncovered a new human-competitive scheduling technique which generalises well across cells of varying sizes. Furthermore, evolved methods are shown to conform to accepted scheduling frameworks without the evolutionary process being explicitly told the form of the desired solution. Evolved solutions are shown to out-perform a human-engineered state-of-the-art benchmark by up to 50%. Finally, the approach is shown to be flexible in that tailored algorithms can be evolved for specific scenarios and corner cases, allowing network operators to create unique algorithms for different deployments, and to postpone the need for costly hardware upgrades.
Databáze: MEDLINE
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