Machine Learning for Terahertz Communication with Human-Implantable Devices

Kieran Sullivan, Martin Tolan

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

1 Citation (Scopus)

Abstract

Network communication is now critical for several different sectors, including transport, manufacturing, agriculture, and healthcare. The fifth generation (5G) of networks can support and further enhance this vertical sector work, but new paradigms and technologies are required to meet increasing expectations. Terahertz communication offers potential in this regard, especially given its high transmission rates. A number of issues must be considered, however, including signal attenuation due to the absorption characteristic of the transference medium. In this paper, we examine a healthcare scenario where communication between transmitter and receiver is carried out at terahertz frequencies. Our results show that when combined with a machine learning mechanism, terahertz communications protocols can be established to reduce signal path losses in the system.

Original languageEnglish
Title of host publication2018 European Conference on Networks and Communications, EuCNC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages293-297
Number of pages5
ISBN (Print)9781538614785
DOIs
Publication statusPublished - 20 Aug 2018
Event2018 European Conference on Networks and Communications, EuCNC 2018 - Ljubljana, Slovenia
Duration: 18 Jun 201821 Jun 2018

Publication series

Name2018 European Conference on Networks and Communications, EuCNC 2018

Conference

Conference2018 European Conference on Networks and Communications, EuCNC 2018
Country/TerritorySlovenia
CityLjubljana
Period18/06/201821/06/2018

Keywords

  • implantable devices
  • machine learning
  • signal path loss
  • terahertz communication

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