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

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

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)80-84
Number of pages5
JournalIEEE Communications Magazine
Volume60
Issue number8
DOIs
Publication statusPublished - 1 Aug 2022
MoE publication typeA1 Journal article-refereed

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