Self-Cognizant Prognostics for the Design and Implementation of Mission-Critical Telemedicine Systems under the Influence of Heavy Rainfall

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

2 Scopus Citations
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Author(s)

  • Bernard Fong
  • Haesik Kim
  • A. C. M. Fong
  • G. Y. Hong
  • K. F. Tsang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)80-84
Journal / PublicationIEEE Communications Magazine
Volume60
Issue number8
Online published4 Jul 2022
Publication statusPublished - Aug 2022

Abstract

This article discusses the design and implementation strategy for highly reliable communication systems that support autonomous ambulances on an unmanned aerial vehicle platform. Statistics show that more accidents occur during heavy rainfall, but radio links that operate in excess of 10 GHz are particularly prone to rain-induced attenuation and depolarization. Maximizing quality of service to provide reliable wireless links for telemedicine systems is therefore an important issue to be thoroughly addressed in optimizing system reliability. A prognostics and network health management framework for automated adjustment of the link and system margins is proposed, based on statistical results of point rainfall attenuation obtained from long-term measurement and scattering with a case study of a 39 GHz signal propagating through rain. The results are applied to a self-cognizant prognostics algorithm for smart autonomous ambulances that support critical operations across difficult terrain. Near forward scattering of 58° and near backward scattering of 175°, as well as perpendicular scattering, were studied. This provides important insights into implementing self-cognizant prognostics system resource management such that 5G-based, as well as moving toward 6G, telemedicine systems can be optimized for reliability given the appropriate system fade margin derived from the measurement results.

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

Self-Cognizant Prognostics for the Design and Implementation of Mission-Critical Telemedicine Systems under the Influence of Heavy Rainfall. / Fong, Bernard; Kim, Haesik; Fong, A. C. M. et al.
In: IEEE Communications Magazine, Vol. 60, No. 8, 08.2022, p. 80-84.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review