Ambient Radio Frequency Sources Research Articles

Energy harvesting system using broadband textile antennas

Taking advantage of the benefits provided by the energy harvesting from ambient radio frequency sources, a multiband receiver is designed and manufactured using broadband antennas constructed with textile materials to be adapted into a Tshirt. The antennas connected to the rectifier allow to capture energy from the environment from four mobile bands: 1700, 1800, 1900 and 2100 MHz, and from the 2400 MHz WiFi band.

A 2.45/5.8 GHz high-efficiency dual-band rectifier for low radio frequency input power

This article proposes a concurrent rectifier for radio frequency (RF) energy harvesting from the popular ambient RF sources wireless fidelity (WiFi) 2.45 and 5.8 GHz bands. A voltage doubler-based converter circuit with the Schottky SMS7630 diode is used, this chosen diode has shown good results for low power levels. To ameliorate the resulting circuit, we used an interdigital capacitor (IDC) instead of a lumped component; and then we added a filter to reject the 3rd harmonics of each operating frequency. A dual-band impedance transformer with a direct current (DC) block function is used and optimized at low input power points for more harvested DC power. The final circuit was, therefore, more efficient and more reliable. The maximum conversion efficiencies obtained from the resulting circuit are about 60.321% for 2.45 GHz and 47.175% for 5.8 GHz at 2 dBm of input power. Compared to other previous rectifiers presented in the literature, our proposed circuit presents high efficiencies at low power levels and at these operating frequencies.

Optimizing Energy Harvesting Decode-and-Forward Relays With Decoding Energy Costs and Energy Storage

Energy harvesting (EH) relay communication systems with decoding energy costs in multiple block cases have not been widely studied. This paper investigates the relay network with a decode-and-forward relay powered by EH. Unlike other works, we consider the relay with energy decoding costs which harvests random energy from both a dedicated transmitter and other ambient radio-frequency (RF) sources. The EH relay adopts a harvest-receive-forward time-switching architecture. We optimize the time fractions of the three phases and the reception rate at the relay to maximize the offline throughput for single and multiple block cases under two EH scenarios. The multi-block optimization problem constitutes a complex non-convex problem, which we decouple into a single block problem with two auxiliary variables determined by an outer optimization problem. The original problem is finally solved at the cost of linear optimization after series of tricks. Several conclusions are derived: (i) energy storage is necessary (unnecessary) when the relay harvests energy from the transmitter (ambient RF sources), (ii) the optimal reception rate remains unchanged, while the optimal time fractions vary with the energy harvested from ambient RF sources leading to different average throughput. We give numerical simulations to verify our theoretical analysis.

Resource Allocation Strategy for D2D-Assisted Edge Computing System With Hybrid Energy Harvesting

Due to the limited battery capacity and computing capability of mobile users, the resource allocation strategy in device-to-device (D2D)-assisted edge computing system with hybrid energy harvesting is investigated in this paper. By employing magnetic induction-based wireless reverse charging technology, mobile user can supplement extra energy from nearby users when the energy harvested from ambient radio frequency sources is about to be exhausted. Moreover, mobile user can not only perform local computation, but also offload computing tasks to nearby users for auxiliary computation through D2D communication links or mobile edge computing (MEC) server under base station (BS) for edge computation. Due to the limited computing resources of MEC server, when the computing capability of the MEC server reaches the maximum value, an adjacent user under another nearby BS can be considered as a relay node. The computing tasks of the remaining users under the previous BS can be transferred to the MEC server with sufficient resources under another nearby BS by establishing D2D relay links. The objective of the resource allocation strategy is to maximize the energy efficiency under the constraints of computation delay and energy harvesting. The resource allocation problem is formulated as a mixed-integer nonlinear programming problem, which is not easy to obtain the optimal solution at low computational complexity. A suboptimal solution is obtained by adopting the quantum-behaved particle swarm optimization (QPSO) algorithm. Simulation results show that the performance of the proposed strategy is superior to other benchmark strategies, and QPSO algorithm can achieve higher energy efficiency than the standard particle swarm optimization algorithm.

On Max–Min Throughput in Backscatter-Assisted Wirelessly Powered IoT

Backscatter communication can potentially find wide Internet of Things (IoT) applications because of its negligible energy consumption. Traditional backscatter communication relies on either dedicated radio-frequency (RF) sources, such as RF identification readers and power beacons, or ambient RF sources, e.g., TV and cellular signals. In this article, we study a backscatter-assisted wirelessly powered IoT system where devices can backscatter when nearby devices are actively transmitting via RF. Our objective is to determine the transmission schedule for all devices that maximizes the minimum system throughput. We formulate such a scheduling problem as a linear program for both linear and random networks. A key step in the formulation is to identify the groups of devices that can simultaneously backscatter without causing interference. Simulation results show that the max-min system throughput in linear and random networks can be increased by 46 and 180 times, respectively, by using the proposed backscatter-assisted schedule as compared with the traditional time-division multiple access (TDMA).

Energy and Spectral Efficient Cognitive Radio Sensor Networks for Internet of Things

Energy and spectral efficient solutions are indispensable to the success of Internet of Things (IoT). The design and development of energy and spectral efficient solutions for IoT are very challenging mainly because of the large-scale deployment of a massive number of sensors and devices. Energy harvesting and cognitive radios (CRs) are considered as promising technologies for energy and spectral efficiency, respectively. In this paper, we propose an energy and spectrum efficient scheme for CR sensor networks (CRSNs). We present an architecture of CRSNs for IoT, in which sensor nodes can access the spectrum opportunistically and harvest energy from ambient radio-frequency sources. We then propose an energy management scheme that consists of: 1) energy-aware mode switching strategy which allows sensor nodes to perform dedicated energy harvesting based on their current energy level and 2) cluster head selection algorithm which considers current and average of past energy levels of sensor nodes to achieve a balance between network performance and lifetime. Furthermore, for reliable intracluster reporting, we propose a channel management strategy to assign the best quality channel to the sensor nodes in terms of stability and reliability. Extensive simulation results demonstrate the effectiveness of the proposed energy and spectrum efficient scheme and show superiority over existing schemes.

SURE: A Novel Approach for Self Healing Battery Starved Users Using Energy Harvesting

Radio Frequency (RF) energy harvesting holds a promising future for energizing low power mobile devices in next generation wireless networks. Harvesting from a dedicated RF energy source acquires much more energy than simply harvesting from ambient RF sources. In this paper, novel Self-healing of Users equipment by RF Energy transfer scheme is introduced between the network operator and battery starved users to heal and extend their battery life time by sending dedicated energy from different sources in order to be aggregated and harvested by starved users. This approach depends on the concept of Energy as a Service where the network operator delivers energy to battery starved users in the next generation networks. A mixed integer non-linear optimization problem is formulated and solved efficiently using three heuristic algorithms. Simulation results prove that sufficient amounts of energy can be delivered to starved users while minimizing their uplink power requirements and guaranteeing a minimum uplink data rate.

Energy Harvesting-Based D2D Communications in the Presence of Interference and Ambient RF Sources

This paper considers a device-to-device (D2D) network with time-splitting protocol, where a D2D transmitter ( $ \text {T}_{\text {x}}$ ) first harvests energy from a multiple-antenna power beacon (PB) and ambient radio frequency sources, and then uses that harvested energy to transmit data to the D2D receiver ( $ \text {R}_{\text {x}}$ ). To improve the energy transfer efficiency, the PB is equipped with multiple antennas for energy transfer, and $ \text {T}_{\text {x}}$ is equipped with multiple antennas for energy harvesting. Two beamforming techniques, called best antenna-based beamforming and optimal beamforming vector, are proposed to use at the PB. We derive novel analytical expressions for the average harvested energy, power outage probability, and the outage probability of the information transfer link, considering the effect of co-channel interference from homogeneous Poison distributed interferes and the short-range propagation model for the path loss. We show that by deploying multiple harvesting energy antennas at $ \text {T}_{\text {x}}$ and by implementing optimal beamforming vector scheme at the PB, the system performance improves substantially. Furthermore, Monte-Carlo simulations are provided and verify the accuracy of our analytical results.

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

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 $\alpha $ -Ginibre point process ( $\alpha $ -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 $\alpha $ -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.

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

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 alpha-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.