Regression-based K nearest neighbours for resource allocation in network slicing

Dandan Yan, Xu Yang, Laurie Cuthbert

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

5 Citations (Scopus)

Abstract

Network slicing is necessary for modern mobile networks to provide flexible service in areas like smart cities, where there are diverse application requirements as well as growth in demand. In this paper, machine learning K-Nearest Neighbours (KNN) is used to match user distribution scenarios stored within a case library to find out the best boundary between slices without having to perform more computational-expensive approaches. The KNN algorithm is used to identify similar cases and the ratio of qualified users (QUR) who obtained required resources is taken as the test performance indicator.The simulation results show that the proposed architecture is capable of effective slice boundary determination and the resource allocation according to that determination gives good results.

Original languageEnglish
Title of host publication2022 Wireless Telecommunications Symposium, WTS 2022
PublisherIEEE Computer Society
ISBN (Electronic)9781728186788
DOIs
Publication statusPublished - 2022
Event21st Annual Wireless Telecommunications Symposium, WTS 2022 - Virtual, Online, United States
Duration: 6 Apr 20228 Apr 2022

Publication series

NameWireless Telecommunications Symposium
Volume2022-April
ISSN (Print)1934-5070

Conference

Conference21st Annual Wireless Telecommunications Symposium, WTS 2022
Country/TerritoryUnited States
CityVirtual, Online
Period6/04/228/04/22

Keywords

  • K-Nearest Neighbors
  • Network Slicing
  • QUR
  • Resource Allocation

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