Stochastic dynamics of determinantal processes by integration by parts

IEEE J. Select. Areas Commun.

Niyato

et al. 2016

Self Cite

RF-enabled wireless power transfer and energy harvesting has recently emerged as a promising technique to provision perpetual energy replenishment for low-power wireless networks. The network devices are replenished by the RF energy harvested from the transmission of ambient RF transmitters, which offers a practical and promising solution to enable selfsustainable communications. This paper adopts a stochastic geometry framework based on the Ginibre model to analyze the performance of self-sustainable communications over cellular networks with general fading channels. Specifically, we consider the point-to-point downlink transmission between an access point and a battery-free device in the cellular networks, where the ambient RF transmitters are randomly distributed following a repulsive point process, called Ginibre α-determinantal point process (DPP). Two practical RF energy harvesting receiver architectures, namely time-switching and power-splitting, are investigated. We perform an analytical study on the RF-powered device and derive the expectation of the RF energy harvesting rate, the energy outage probability and the transmission outage probability over Nakagami-m fading channels. These are expressed in terms of so-called Fredholm determinants, which we compute efficiently with modern techniques from numerical analysis. Our analytical results are corroborated by the numerical simulations, and the efficiency of our approximations is demonstrated. In practice, the accurate simulation of any of the Fredholm determinant appearing in the manuscript is a matter of seconds. An interesting finding is that a smaller value of α (corresponding to larger repulsion) yields a better transmission outage performance when the density of the ambient RF transmitters is small. However, it yields a lower transmission outage probability when the density of the ambient RF transmitters is large. We also show analytically that the power-splitting architecture outperforms the time-switching architecture in terms of transmission outage performances. Lastly, our analysis provides guidelines for setting the time-switching and power-splitting coefficients at their optimal values. Index terms-Wireless energy harvesting, self-sustainable communications, wireless powered communication networks, simultaneous wireless information and power transfer, Nakagami-m fading, Internet of things, time-switching, powersplitting, determinantal point process, Ginibre model.

Section: Analytical Formulasmentioning

confidence: 99%

Self-Sustainable Communications With RF Energy Harvesting: Ginibre Point Process Modeling and Analysis

IEEE J. Select. Areas Commun.

Niyato

et al. 2016

Self Cite

“…The simulation of α-DPPs when α = −1/j, j ∈ N, is done by using the Schmidt orthogonalization algorithm developed in full generality in [41], and specifically in [25] for the Ginibre point process. The simple generalization to α = −1/j can be found in the recent survey [42], and additional details on DPP can be found in [43].…”

Section: ) Estimation By Simulation: the Different Theoretical Perfomentioning

confidence: 99%

Performance Analysis of Ambient RF Energy Harvesting with Repulsive Point Process Modeling

IEEE Trans. Wireless Commun.

Privault

et al. 2015

Self Cite

Ambient RF (Radio Frequency) energy harvesting technique has recently been proposed as a potential solution to provide proactive energy replenishment for wireless devices. This paper aims to analyze the performance of a battery-free wireless sensor powered by ambient RF energy harvesting using a stochastic geometry approach. Specifically, we consider the point-to-point uplink transmission of a wireless sensor in a stochastic geometry network, where ambient RF sources, such as mobile transmit devices, access points and base stations, are distributed as a Ginibre α-determinantal point process (DPP).The DPP is able to capture repulsion among points, and hence, it is more general than the Poisson point process (PPP). We analyze two common receiver architectures: separated receiver and time-switching architectures. For each architecture, we consider the scenarios with and without co-channel interference for information transmission. We derive the expectation of the RF energy harvesting rate in closed form and also compute its variance. Moreover, we perform a worst-case study which derives the upper bound of both power and transmission outage probabilities. Additionally, we provide guidelines on the setting of optimal time-switching coefficient in the case of the time-switching architecture. Numerical results verify the correctness of the analysis and show various tradeoffs between parameter setting. Lastly, we prove that the sensor is more efficient when the distribution of the ambient sources exhibits stronger repulsion.

“…Simulation of α-DPPs when α < 0 is done by using the Schmidt orthogonalization algorithm developed in full generality in [20], and specifically in [12] for the Ginibre point process. The simple generalization to all α < 0 can be found in the recent survey [21], and additional details on DPP can be found in [22].…”

Section: A Ginibre α-Determinantal Point Processmentioning

confidence: 99%

Performance analysis of ambient RF energy harvesting: A stochastic geometry approach

2014 IEEE Global Communications Conference

Privault

et al. 2014

Self Cite