Learning congestion over millimeter-wave channels

Luis Diez, Ramon Aguero, Alfonso Fernandez, Yasir Zaki, Muhammad Khan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper studies how learning techniques can be used by the congestion control algorithms employed by transport protocols over 5G wireless channels, in particular millimeter waves. We show how metrics measured at the transport layer might be valuable to ascertain the congestion level. In situations characterized by a high correlation between such parameters and the actual congestion, it is observed that the performance of unsupervised learning methods is comparable to supervised learning approaches. Exploiting the ns-3 platform to perform an in-depth, realistic assessment, allows us to study the impact of various layers of the protocol stack. We also consider different scheduling policies to discriminate whether the allocation of radio resources impacts the performance of the proposed scheme.

Original languageEnglish (US)
Title of host publication2020 16th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728197227
DOIs
StatePublished - Oct 12 2020
Event16th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2020 - Virtual, Thessaloniki, Greece
Duration: Oct 12 2020Oct 14 2020

Publication series

NameInternational Conference on Wireless and Mobile Computing, Networking and Communications
Volume2020-October
ISSN (Print)2161-9646
ISSN (Electronic)2161-9654

Conference

Conference16th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2020
CountryGreece
CityVirtual, Thessaloniki
Period10/12/2010/14/20

Keywords

  • 5G
  • congestion control
  • machine learning
  • millimeter waves
  • network simulation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Software

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