Modeling energy availability in RF Energy Harvesting Networks

Daniela Oliveira, Rodolfo Oliveira

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

5 Citations (Scopus)

Abstract

In a Radio Frequency (RF) Energy Harvesting Network (RF-EHN) the nodes have the capability of converting received electromagnetic RF signals in energy. Traditionally, the RF signals are provided by high power transmitters operating in the neighborhood of the harvesters. Admitting that the transmitters are spatially distributed according to a spatial Poisson process, we start by characterizing the distribution of the RF power received by an energy harvester node. Considering path loss and fading effects, we provide a theoretical approximation for the distribution of the received RF power. Using the distribution of the received RF power, we derive the probability of a node having enough energy to transmit a packet after a given amount of battery charging time. The numerical results obtained with the proposed analysis are close to the ones obtained through simulation, which confirms the accuracy of the proposed analysis.

Original languageEnglish
Title of host publicationISWCS 2016 - 13th International Symposium on Wireless Communication Systems, Proceedings
Place of PublicationNew York, USA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages383-387
Number of pages5
Volume2016-October
ISBN (Electronic)978-150902061-4
ISBN (Print)978-1-5090-2062-1
DOIs
Publication statusPublished - 2016
Event13th International Symposium on Wireless Communication Systems, ISWCS 2016 - Poznan, Poland
Duration: 20 Sept 201623 Sept 2016

Publication series

NameInternational Symposium on Wireless Communication Systems
PublisherIEEE
Volume2016-October
ISSN (Print)2154-0217

Conference

Conference13th International Symposium on Wireless Communication Systems, ISWCS 2016
Country/TerritoryPoland
CityPoznan
Period20/09/1623/09/16

Keywords

  • Performance Analysis
  • RF Energy Harvesting
  • Stochastic Modeling

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