Ambient radio frequency (RF) energy harvesting technique has recently been proposed as a potential solution for providing 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 @math -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 RF-powered sensor performs better when the distribution of the ambient sources exhibits stronger repulsion.
Geometry approaches have been applied to analyze RF energy harvesting performance in cellular network @cite_34 , cognitive radio network @cite_25 , and relay network @cite_12 @cite_24 @cite_21 . The authors in @cite_34 investigate tradeoffs among transmit power and density of mobiles and wireless charging stations which are both distributed as a homogeneous Poisson Point Process (PPP). Energy harvesting relay network has been mostly analyzed. @cite_25 , the authors study a cognitive radio network where primary and secondary networks are distributed as independent homogeneous PPPs. The secondary network is powered by the energy opportunistically harvested from nearby transmitters in the primary network. Under the outage probability requirements for both coexisting networks, the maximum throughput of the secondary network is analyzed. The study in @cite_12 analyzes the impact of cooperative density and relay selection in a large-scale network with transmitter-receiver pairs distributed as a PPP. Reference @cite_24 investigates a decode-and-forward relay network with multiple source-destination pairs. Under the assumption that the relay nodes are distributed as a PPP, the network outage probability has been characterized. The studies in @cite_21 investigates network performance of a two-way network-coded cooperative network, where the source, destination and RF-powered relay nodes are modeled as three independent PPPs.
@cite_21 @cite_24 @cite_34 @cite_25 @cite_12
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In this paper, the performance of opportunistic relay selection (ORS) in a cognitive radio is analyzed over flat Rayleigh fading channels. Data transmission between source and destination is assumed to be entirely performed via the relays. Relay nodes are assumed to have ability to harvest energy from the source signal and use that harvested energy to forward the information to the destination. Specifically, we derive an exact expression for the outage probability of the secondary system considering the maximum transmit power at the secondary transmitter and relays, energy harvesting efficiency at relays, and interference constraint at the primary receiver. Under the assumption of perfect channel state information at the receivers, we evaluate the outage probability of a cognitive radio system with ORS and energy harvesting. Wireless networks can be self-sustaining by harvesting energy from ambient radio-frequency (RF) signals. Recently, researchers have made progress on designing efficient circuits and devices for RF energy harvesting suitable for low-power wireless applications. Motivated by this and building upon the classic cognitive radio (CR) network model, this paper proposes a novel method for wireless networks coexisting where low-power mobiles in a secondary network, called secondary transmitters (STs), harvest ambient RF energy from transmissions by nearby active transmitters in a primary network, called primary transmitters (PTs), while opportunistically accessing the spectrum licensed to the primary network. We consider a stochastic-geometry model in which PTs and STs are distributed as independent homogeneous Poisson point processes (HPPPs) and communicate with their intended receivers at fixed distances. Each PT is associated with a guard zone to protect its intended receiver from ST's interference, and at the same time delivers RF energy to STs located in its harvesting zone. Based on the proposed model, we analyze the transmission probability of STs and the resulting spatial throughput of the secondary network. The optimal transmission power and density of STs are derived for maximizing the secondary network throughput under the given outage-probability constraints in the two coexisting networks, which reveal key insights to the optimal network design. Finally, we show that our analytical result can be generally applied to a non-CR setup, where distributed wireless power chargers are deployed to power coexisting wireless transmitters in a sensor network. Cognitive radio networks using relays have been introduced to enhance secondary network performance by having secondary users (SUs) transmit in two hops. This paper develops a framework for a random cognitive relay network (RCRN) where the primary, secondary, and relay nodes are randomly distributed according to Poisson point processes (PPPs) to investigate the effect of the random nature of the relays on secondary outage performance. We first derive the optimal relay location in terms of the outage probability of the secondary network. In addition, we provide the outage probability of the RCRN based on the optimal relay selection strategy that compares the instantaneous channel state information (CSI) of all relays. We then propose an efficient relay selection strategy that reduces the selection complexity and the feedback burden by limiting candidate relays to those located close to the optimal relay location. This paper sheds new light on the design of an efficient RCRN with respect to elements such as the effects of network parameters and the candidate relay set size on the RCRN outage probability. In this paper, we consider a wireless powered cooperative network, in which a source with constant power supply communicates with a destination under the assistance of an energy harvesting (EH) relay. From signals of the source, the relay can perform EH and information decoding simultaneously by using the power splitting (PS) technique. To increase the spectrum efficiency of the system and save energy consumption at the relay, an incremental decode-and-forward (IDF) relaying protocol is adopted to forward information. Inspired by the features of the IDF protocol, we propose a new energy harvesting and use strategy, named adaptive harvest-store-use (AHSU). In this proposed strategy, the relay adaptively sets its PS ratio according to a one-bit feedback from the destination, the channel estimation result for the source-to-relay link, and the relay’s energy status. A finite-state Markov chain (MC) is employed to model the charging discharging behavior of the relay’s battery. The steady-state distribution of the MC is first derived, and then used to calculate the exact outage probability. In order to gain further insights, we investigate the outage performance of the system when the transmit signal-to-noise ratio of the source is high. Based on the asymptotic outage probability expression, the diversity order and coding gain are characterized, which demonstrates that a full diversity order is achieved by our proposed AHSU strategy in the considered network.