Investigating the Characterization of Energy Availability in RF Energy Harvesting Networks

Oliveira, Daniela and Oliveira, Rodolfo (2020) Investigating the Characterization of Energy Availability in RF Energy Harvesting Networks. In: Theory and Practice of Mathematics and Computer Science Vol. 3. B P International, pp. 1-17. ISBN 978-93-90431-35-9

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In this work we are particularly focused on the characterization of the RF power received by each harvester and
its impact in terms of the probability of accumulating enough energy to transmit a packet. The multiple nodes
forming a Radio Frequency (RF) Energy Harvesting Network (RF-EHN) have the capability of converting
received electromagnetic RF signals in energy that can be used to power a network device (the energy
harvester). Traditionally the RF signals are provided by high power transmitters (e.g., base stations) operating in
the neighborhood of the harvesters. Recently, RF energy harvesting has attracted much attention and many
efforts are being dedicated to develop innovative RF energy harvesting technologies as well as to investigate the
performance of the networks formed by the harvesting devices. 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 Gamma shadowing and Rayleigh fading, we show that the
received RF power can be approximated by the sum of multiple Gamma distributions with different scale and
shape parameters. 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 charging time. The RF power distribution and the
probability of a harvester having enough energy to transmit a packet are validated through simulation. The
numerical results obtained with the proposed analysis are close to the ones obtained through simulation, which
confirms the accuracy of the proposed analysis.

Item Type: Book Section
Subjects: Eprint Open STM Press > Computer Science
Depositing User: Unnamed user with email
Date Deposited: 10 Nov 2023 09:44
Last Modified: 10 Nov 2023 09:44

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