What can Machine Learning do for Radio Spectrum Management?

Ebtesam Almazrouei, Gabriele Gianini, Nawaf Almoosa, Ernesto Damiani

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

2 Scopus citations

Abstract

The opening of the unlicensed radio spectrum creates new opportunities and new challenges for communication technology that can be faced by Machine Learning techniques. In this work, we discuss the potential benefits and the challenges with reference to the recent research developments in this area. Applications go from channel estimation to Signal quality control, and from signal classification to action control. We survey Machine learning and Deep Learning algorithms with possible radio applications and highlight the corresponding challenges.

Original languageBritish English
Title of host publicationQ2SWinet 2020 - Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks
Pages15-21
Number of pages7
ISBN (Electronic)9781450381208
DOIs
StatePublished - 16 Nov 2020
Event19th ACM symposium on QoS and Security for Wireless and Mobile Networks, Q2SWinet 2020 - Alicante, Spain
Duration: 16 Nov 202020 Nov 2020

Publication series

NameQ2SWinet 2020 - Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks

Conference

Conference19th ACM symposium on QoS and Security for Wireless and Mobile Networks, Q2SWinet 2020
Country/TerritorySpain
CityAlicante
Period16/11/2020/11/20

Keywords

  • machine learning
  • radio signals
  • wireless communication

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