Machine Learning for Terahertz Communication with Human-Implantable Devices

Sullivan, Kieran and Tolan, Martin (2018) Machine Learning for Terahertz Communication with Human-Implantable Devices. In: 2018 European Conference on Networks and Communications, EuCNC 2018 :. 2018 European Conference on Networks and Communications, EuCNC 2018 . Institute of Electrical and Electronics Engineers Inc., SVN, pp. 293-297. ISBN 9781538614785

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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.

Item Type:	Book Section
Additional Information:	Funding Information: ACKNOWLEDGMENT The work in this paper has been facilitated by the CogNet project (671625), which is funded under the European Commission’s H2020 5G-PPP initiative. Publisher Copyright: © 2018 IEEE.
Uncontrolled Keywords:	/dk/atira/pure/subjectarea/asjc/1700/1705
Departments or Groups:
Depositing User:	Admin SSL
Date Deposited:	19 Oct 2022 23:14
Last Modified:	17 Jul 2023 03:45
URI:	http://repository-testing.wit.ie/id/eprint/4853

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