Radio Frequency Energy Harvesting Capability Research Articles

A reconfigurable wideband MIMO antenna combines RF energy harvesting

SummaryThis article introduces a novel and groundbreaking approach combining multiple‐input‐multiple‐output (MIMO) technology with radio frequency (RF) energy harvesting. The proposed antenna consists of two semi‐circular monopole antenna components, optimized with dimensions of 89 × 51.02 × 1.6 mm3, that share a common ground plane to achieve MIMO characteristics. A series of split‐ring resonators on the ground plane significantly enhances the isolation between the two radiating components. Band‐notched features are performed in the 3.5 GHz WiMAX and 5.5 GHz WLAN bands through modified C‐shaped slots in the radiating patch and two rectangular split‐ring resonators serving as parasitic devices near the feed line. The reconfiguration of band‐notching is made possible by controlling the modes of the embedded PIN diodes. The two antenna elements maintain mutual coupling below −18 dB from 1.5–13 GHz, achieving an impressive 158.62% impedance bandwidth. The antenna's efficiency and gain experience significant drop, indicating effective interference suppression at the center frequencies of the notch bands, and its performance in MIMO systems is assessed through parameters including envelope correlation coefficient, port isolation, radiation patterns, efficiency, gain, and diversity gain. The simulated properties of the designed antenna closely align with the measured outcomes, demonstrating its reliability and consistency. Moreover, the article evaluates the antenna's potential for RF energy harvesting, achieving a maximum harvested energy of 4.88 V. This proposed antenna can be used in multiple applications, like wideband, band‐notching MIMO, and RF energy harvesting. This proposed antenna is an efficient, reconfigurable wideband MIMO antenna with novel RF energy harvesting capability.

A novel relay‐aided centralized cooperative spectrum sensing scheme using radio frequency energy harvesting

SummaryIn a cooperative spectrum sensing (CSS) network, the primary signal can be used as a radio frequency (RF) source in order to power the energy‐constrained sensor nodes of the secondary network. This work presents a novel hybrid model combining an optimal relay selection scheme to incorporate RF energy harvesting in a centralized CSS network. The secondary users, which are equipped with RF energy harvesting capabilities, act as relays in order to forward the sensing information to a fusion center. Here, we have derived an enhanced multi‐relay selection strategy to maximize the end‐to‐end signal‐to‐noise ratio of the links. Furthermore, a new voting rule is proposed based on the generalized K‐out‐of‐M rule, such that it minimizes our objective error function. The performance analysis of our proposed model is presented with respect to the flexible relay positions. We have used complementary receiver operating characteristic curves for analyzing the detection performance of the CSS model with our derived voting rule. Simulation results using MATLAB show that the proposed model gives a better detection probability and a smaller error rate than some related existing works.

Deep Reinforcement Learning Optimal Transmission Algorithm for Cognitive Internet of Things With RF Energy Harvesting

Spectrum scarcity and energy limitation are becoming two critical issues in designing Internet of Things (IoT). As two promising technologies, cognitive radio (CR) and radio frequency (RF) energy harvesting can be used together to improve both energy and spectral efficiency. In this paper, an optimal transmission problem in a cognitive IoT (CIoT) with RF energy harvesting capability is investigated, where the optimization problem is formulated as a Markov decision process (MDP) without any priori-knowledge. Considering that the channel activity states of primary user network (PUN), RF energy arrival process and channel information are not available in advance, a deep reinforcement learning (DRL) based deep deterministic policy gradient (DDPG) algorithm is proposed to deal with the dynamic uplink access, working mode selection and continuous power allocation to maximize a long term uplink throughput. The simulation results show that the proposed algorithm is valid and efficient to achieve better performances when compared with deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> -network (DQN) based, myopic and random algorithms.

Asynchronous Ad Hoc Networks With Wireless Powered Cognitive Communications

Over the recent years, the proliferation of smart devices and their applications has led to a rapid evolution of the concept of the Internet of Things (IoT), advancing large scale machine type networks which are characterized by sporadic transmissions of short packets. In contrast to typical communication models and in order to capture a realistic IoT environment, we study an asynchronous channel access performed by a primary ad hoc network underlaid with a cognitive secondary wireless-powered ad hoc network. Specifically, we consider that the primary transmitters are connected to the power grid and employ asynchronous transmissions. On the other hand, the cognitive secondary transmitters have radio frequency energy harvesting capabilities, and their asynchronous channel access is established based on certain energy and interference-based criteria. We model this sporadic channel traffic with time–space Poisson point processes and by using tools from stochastic geometry, we provide an analytical framework for the performance of this asynchronous system. In particular, we provide closed-form expressions for the information coverage probability and the spatial throughput for both networks and we derive the meta distribution of the signal-to-interference-plus-noise ratio. Finally, we present numerical results and provide important insights behind the main system parameters.

Adaptive scheduling to enhance data security and energy efficiency on energy harvesting platform

Radio-frequency (RF)-based energy harvesting platforms are appealing for always-on applications due to its radio frequency energy harvesting capability. However, the current RF-based energy harvesting capability is limited by many factors including harvesting distance, device constraints, communication, and security. RF-based energy harvesting platforms tend to harvest less energy as the device is further away, and impacts the ability to execute its own applications properly. The limited storage of energy on such platforms will make it even challenging to implement energy-hungry security functions on top of basic functions such as sensing, computing and transmitting. Therefore, by default, a lot of RF-based energy harvesting platforms have no basic security functions implemented, which is not acceptable for security-sensitive applications such as biomedical applications.In this paper, we propose an adaptive light-weight scheduling mechanism that aims to balance data security and energy efficiency for application on RF-based energy-harvesting platforms. We will then prototype this scheduler on a Wireless Identification and Sensing Platform (WISP) device. The results in this paper will test this scheduling mechanism in action on a WISP device, along with use of a sensor to determine energy impact on the device, a deadline-based mechanism for data distribution, and finally comparing these cases with average voltage and total time.

Optimization of energy-constrained wireless powered communication networks with heterogeneous nodes

In this paper, we study wireless networks where nodes have two energy sources, namely a battery and radio frequency (RF) energy harvesting circuitry. We formulate two optimization problems with different objective functions, namely maximizing the sum throughput and maximizing the minimum throughput, for enhanced fairness. Furthermore, we show the generality of the proposed system model through characterizing the conditions under which the two formulated optimization problems can be reduced to the corresponding problems of different known wireless networks, namely, conventional wireless networks (battery-powered) and wireless powered communications networks (WPCNs) with only RF energy harvesting nodes. In addition, we introduce WPCNs with two types of nodes, with and without RF energy harvesting capability, in which the nodes without RF energy harvesting are utilized to enhance the sum throughput, even beyond WPCNs with all energy harvesting nodes. We establish the convexity of all formulated problems which opens room for efficient solution using standard techniques. Our numerical results show that the two types of wireless networks, namely WPCNs with only RF energy harvesting nodes and conventional wireless networks, are considered, respectively, as lower and upper bounds on the performance of the generalized problem setting in terms of the maximum sum throughput and the maxmin throughput. Moreover, the results reveal new insights and throughput-fairness trade-offs unique to our new problem setting.

Spatial Domain Simultaneous Information and Power Transfer for MIMO Channels

In this paper, we theoretically investigate a new technique for simultaneous information and power transfer (SWIPT) in multiple-input multiple-output (MIMO) point-to-point with radio frequency energy harvesting capabilities. The proposed technique exploits the spatial decomposition of the MIMO channel and uses the eigenchannels either to convey information or to transfer energy. In order to generalize our study, we consider channel estimation error in the decomposition process and the interference between the eigenchannels. An optimization problem that minimizes the total transmitted power subject to maximum power per eigenchannel, information and energy constraints is formulated as a mixed-integer nonlinear program and solved to optimality using mixed-integer second-order cone programming. A near-optimal mixed-integer linear programming solution is also developed with robust computational performance. A polynomial complexity algorithm is further proposed for the optimal solution of the problem when no maximum power per eigenchannel constraints are imposed. In addition, a low polynomial complexity algorithm is developed for the power allocation problem with a given eigenchannel assignment, as well as a low-complexity heuristic for solving the eigenchannel assignment problem.

Performance Optimization for Cooperative Multiuser Cognitive Radio Networks with RF Energy Harvesting Capability

We study the performance optimization problem for a cognitive radio network with radio frequency (RF) energy harvesting capability for secondary users. In such networks, the secondary users are able to not only transmit packets on a channel licensed to a primary user when the channel is idle, but also harvest RF energy from the primary users' transmissions when the channel is busy. Specifically, we propose a system model where the secondary users are able to cooperate to maximize the overall network throughput through sensing a set of common channels. We first consider the case where the secondary users cooperate in a TDMA fashion and propose a novel solution based on a learning algorithm to find optimal channel access policies for the secondary users. Then, we examine the case where the secondary users cooperate in a decentralized manner and we formulate the cooperative decentralized optimization problem as a decentralized partially observable Markov decision process (DEC-POMDP). To solve the cooperative decentralized stochastic optimization problem, we apply a decentralized learning algorithm based on the policy gradient and the Lagrange multiplier method to obtain optimal channel access policies. Extensive performance evaluation is conducted and it shows the efficiency and the convergence of the learning algorithms.

Wireless Networks With RF Energy Harvesting: A Contemporary Survey

Radio frequency (RF) energy transfer and harvesting techniques have recently become alternative methods to power the next generation wireless networks. As this emerging technology enables proactive energy replenishment of wireless devices, it is advantageous in supporting applications with quality of service (QoS) requirement. In this paper, we present an extensive literature review on the research progresses in wireless networks with RF energy harvesting capability, referred to as RF energy harvesting networks (RF-EHNs). First, we present an overview of the RF-EHNs including system architecture, RF energy harvesting techniques and existing applications. Then, we present the background in circuit design as well as the state-of-the-art circuitry implementations, and review the communication protocols specially designed for RF-EHNs. We also explore various key design issues in the development of RF-EHNs according to the network types, i.e., single-hop network, multi-antenna network, relay network and cognitive radio network. Finally, we envision some open research directions.

Opportunistic Channel Access and RF Energy Harvesting in Cognitive Radio Networks

Radio frequency (RF) energy harvesting is a promising technique to sustain operations of wireless networks. In a cognitive radio network, a secondary user can be equipped with RF energy harvesting capability. In this paper, we consider such a network where the secondary user can perform channel access to transmit a packet or to harvest RF energy when the selected channel is idle or occupied by the primary user, respectively. We present an optimization formulation to obtain the channel access policy for the secondary user to maximize its throughput. Both the case that the secondary user knows the current state of the channels and the case that the secondary knows the idle channel probabilities of channels in advance are considered. However, the optimization requires model parameters (e.g., the probability of successful packet transmission, the probability of successful RF energy harvesting, and the probability of channel to be idle) to obtain the policy. To obviate such a requirement, we apply an online learning algorithm that can observe the environment and adapt the channel access action accordingly without any a prior knowledge about the model parameters. We evaluate both the efficiency and convergence of the learning algorithm. The numerical results show that the policy obtained from the learning algorithm can achieve the performance in terms of throughput close to that obtained from the optimization.

Resource allocation in wireless networks with RF energy harvesting and transfer

Radio frequency (RF) energy harvesting and transfer techniques have recently become alternative methods to power the next generation of wireless networks. As this emerging technology enables proactive replenishment of wireless devices, it is advantageous in supporting applications with quality-of-service (QoS) requirement. This article focuses on the resource allocation issues in wireless networks with RF energy harvesting capability, referred to as RF energy harvesting networks (RF-EHNs). First, we present an overview of the RF-EHNs, followed by a review of a variety of issues regarding resource allocation. Then, we present a case study of designing in the receiver operation policy, which is of paramount importance in the RF-EHNs. We focus on QoS support and service differentiation, which have not been addressed by previous literatures. Furthermore, we outline some open research directions.

Dynamic spectrum access in cognitive radio networks with RF energy harvesting

Spectrum efficiency and energy efficiency are two critical issues in designing wireless networks. Through dynamic spectrum access, cognitive radios can improve the spectrum efficiency and capacity of wireless networks. On the other hand radio frequency (RF) energy harvesting has emerged as a promising technique to supply energy to wireless networks and thereby increase their energy efficiency. Therefore, to achieve both spectrum and energy efficiencies, the secondary users in a cognitive radio network (CRN) can be equipped with the RF energy harvesting capability, and such a network can be referred to as an RF-powered cognitive radio network. In this article we provide an overview of the RF-powered CRNs and discuss the challenges that arise for dynamic spectrum access in these networks. Focusing on the trade-off among spectrum sensing, data transmission, and RF energy harvesting, we then discuss the dynamic channel selection problem in a multi-channel RF-powered CRN. In the RF-powered CRN a secondary user can adaptively select a channel to transmit data when the channel is not occupied by any primary user. Alternatively, the secondary user can harvest RF energy for data transmission if the channel is occupied. The optimal channel selection policy of the secondary user can be obtained by formulating a Markov decision process (MDP) problem. We present some numerical results obtained by solving this MDP problem.