Exact Performance Analysis of Ambient RF Energy Harvesting Wireless Sensor Networks With Ginibre Point Process

• Published in: IEEE journal on selected areas in communications Vol. 34; no. 12; pp. 3769 - 3784
• Main Authors: Han-Bae Kong, Flint, Ian, Ping Wang, Niyato, Dusit, Privault, Nicolas
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-12-2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Energy efficiency; Energy harvesting; Fading channels; Ginibre point process; Green communications; Performance assessment; Power system faults; Power system restoration; Radio frequency; Remote sensors; repulsive point process; RF signals; stochastic geometry; Wireless networks; Wireless sensor networks
• ISSN: 0733-8716; 1558-0008
• DOI: 10.1109/JSAC.2016.2621360

Ambient radio frequency (RF) energy harvesting methods have drawn significant interests due to their ability to provide energy to wireless devices from ambient RF sources. This paper considers ambient RF energy harvesting wireless sensor networks where a sensor node transmits data to a data sink using the energy harvested from the signals transmitted by the ambient RF sources. We analyze the performance of the network, i.e., the mean of the harvested energy, the power outage probability, and the transmission outage probability. In many practical networks, the locations of the ambient RF sources are spatially correlated and the ambient sources exhibit repulsive behaviors. Therefore, we model the spatial distribution of the ambient sources as an α-Ginibre point process (α-GPP), which reflects the repulsion among the RF sources and includes the Poisson point process as a special case. We also assume that the fading channel is Nakagami-m distributed, which also includes Rayleigh fading as a particular case. In this paper, by exploiting the Laplace transform of the α-GPP, we introduce semi-closed-form expressions for the considered performance metrics and provide an upper bound of the power outage probability. The derived expressions are expressed in terms of the Fredholm determinant, which can be computed numerically. In order to reduce the complexity in computing the Fredholm determinant, we provide a simple closed-form expression for the Fredholm determinant, which allows us to evaluate the Fredholm determinant much more efficiently. The accuracy of our analytical results is validated through simulation results.