Resource Allocation Challenges and Strategies for RF-Energy Harvesting Networks Supporting QoS (original) (raw)
Related papers
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 literature. Furthermore, we outline some open research directions.
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) requirements. 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 networks, multi-antenna networks, relay networks, and cognitive radio networks. Finally, we envision some open research directions.
RF Energy Harvesting Wireless Networks: Challenges And Opportunities
Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 2021
Energy harvesting wireless networks is one of the most researched topics in this decade, both in industry and academia, as it can offer self-sustaining sensor networks. With RF energy harvesting (RF-EH) embedded, the sensors can operate for extended periods by harvesting energy from the environment or by receiving it as an Energy signal from a hybrid base station (HBS). Thus, providing sustainable solutions for managing massive numbers of sensor nodes. However, the biggest hurdle of RF energy is the low energy density due to spreading loss. This paper investigates the RF-EH node hardware and design essentials, performance matrices of RF-EH. Power management in energy harvesting nodes is discussed. Furthermore, an information criticality algorithm is proposed for critical and hazardous use cases. Finally, some of the RF-EH applications and the opportunities of 5G technologies for the RF-EH are introduced.
A Framework for Optimizing the Process of Energy Harvesting from Ambient RF Sources
International Journal of Electrical and Computer Engineering (IJECE), 2018
Energy harvesting has been an active research topic in the past half a decade with respect to wireless networks. We reviewed some of the recent techniques towards improving energy harvesting performance to find that there is a large scope of improvement in terms of optimization and addressing problems pertaining to low-powered communicating mobile nodes. Therefore, we present a framework for identifying available RF sources of energy and constructing a robust link between the energy source and the mobile device. We apply linear optimization approach to enhance the performance of energy harvesting. Probabilility theory is used for identification of event loss in the presence of different number of nodes as well as node distances. The objective of the proposed system is to offer better availability of RF signals as well as better probability of energy harvesting for mobile devices. The proposed technique is also found to be computationally cost effective. 1. INTRODUCTION With the evolution of cloud computing, there is a sense of pervasiveness in existing applications, be it a small scale or large scale. Such form of pervasive products and services offer significant saving of time and enhances productivity. To access such forms of products or services, normally smart computing devices are used. Preferences are given for mobile devices e.g. laptops, smart phones, sensors, etc. All these devices are equipped with standard hardware circuitry design depending upon various applications that control its communication operation and processing operation [1], [2]. Power module in such hardware acts as a bridge between communication and computation and hence energy modeling is so important in networking [3]. Energy is one of the essential assets as resource for every communicating device globally and affects directly the communication performance [4]. Energy is directly proportional to communication performance, which means more energy to ensure better networking services. But it should be also known that energy is also one of the limited resources within such communication nodes. Energy is required for every essential communication process e.g. transmitting/receiving data, amplifying signal, data aggregation etc. It is also required for internal processing within the communicating nodes. Till date, the biggest challenge is to determine the points where the depletion rate of the energy is faster. Presence of interference, scattering, and fading degrades the channel and potentially affects the node performance too. In such error-prone networks, a node will be required to expend extra amount of energy to participate in the data delivery process. Therefore, traffic engineering significantly affects the energy dissipation of a wireless node to a very large extent and
Computer Networks, 2015
ABSTRACT Green radio communications has received a lot of attention in recent years due to its impact on telecom business, technology and environment. On the other hand, energy harvesting communication has emerged as a potential candidate to reduce the communication cost by tackling the problem in a contrasting fashion. While green communication techniques focus on minimizing the use of radio resources, energy harvesting communication relies on environment friendly techniques to generate energy from renewable resources and on effective use of the stored energy under the condition that there is always energy available when required. Thus, the focus migrates from minimization of energy to optimal time domain ’distribution’ of energy, which causes a paradigm shift in radio resource allocation research.
A new approach to design of RF energy harvesting system to enslave wireless sensor networks
ICT Express, 2018
In trying to reach the goal of controlling the environment, recent years have seen the rapid emergence of Wireless Sensors Networks (WSN). Nevertheless, the lifetime of sensor nodes shows a strong dependence on battery capacity. Recently energy harvesting techniques have been considered to allow the use of WSN in the "deploy and forget" mode. This paper proposes an assessment of the performance of a WSN enslaved to an optimized Radiofrequency Energy Harvesting System (REHS). The energy budget of a sensor node in a Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol is quantified and used to evaluate the performance of the WSN.
Smart RF energy harvesting communications: challenges and opportunities
IEEE Communications Magazine, 2015
RF energy harvesting (RFH) is emerging as a potential method for the proactive energy replenishment of next generation wireless networks. Unlike other harvesting techniques that depend on the environment, RFH can be predictable or on demand, and as such it is better suited for supporting quality-of-service-based applications. However, RFH efficiency is scarce due to low RF-to-DC conversion efficiency and receiver sensitivity. In this article, we identify the novel communication techniques that enable and enhance the usefulness of RFH. Backed by some experimental observations on RFH and the current state of the art, we discuss the challenges in the actual feasibility of RFH communications, new research directions, and the obstacles to their practical implementation.
Dynamic power allocation and scheduling for MIMO RF energy harvesting wireless sensor platforms
TELKOMNIKA Telecommunication, Computing, Electronics and Control, 2021
Radio frequency (RF) energy harvesting systems are enabling new evolution towards charging low energy wireless devices, especially wireless sensor networks (WSN). This evolution is sparked by the development of lowenergy micro-controller units (MCU). This article presents a practical multiple input multiple output (MIMO) RF energy-harvesting platform for WSN. The RF energy is sourced from a dedicated access point (AP). The sensor node is equipped with multiple antennas with diverse frequency responses. Moreover, the platform allows for simultaneous information and energy transfer without sacrificing system duplexity, unlike time-switching RF harvesting systems where data is transmitted only for a portion of the total transmission duty cycle, or power-splitting systems where the power difference between the information signal (IS) and energy signal (ES) is neglected. The proposed platform addresses the gap between those two. Furthermore, system simulation and two energy scheduling methods between AP and sensor node (SN) are presented, namely, Continuous power stream (CPS) and intermittent power stream (IPS).
Optimizing Data Rate and Energy Delivery in Heterogeneous Radio Frequency Harvesting Networks
2020
Wireless Power Transfer (WPT) will be a key enabler of Internet-of-Things (IoT) networks that consist of low-power devices that harvest energy from Radio Frequency (RF) signals emitted by base stations or access points. Hence, future networks will likely have both low-power RF-energy harvesting devices as well as legacy users such as laptops. In particular, both devices and users will share the same wireless channel to receive RF energy as well as transmit data. To ensure they share the spectrum efficiently, this thesis considers resource allocation problems in Orthogonal Frequency Division Multiple access (OFDMA) networks. In particular, it considers sub-band/sub-carrier allocation problems that aim to meet the requirement of low-power devices and legacy users when they co-exist in the same network. Specifically, for low-power devices, developed solutions must ensure low-power devices receive sufficient energy to collect samples or have sufficient energy to transmit frequently to a gateway. Similarly, these solutions must ensure legacy users are able to transmit at a given data rate. The first resource allocation problem concerns two-tier OFDMA based Heterogeneous Networks (HetNets). Specifically, it aims to minimize the downlink sum transmit power of both femto and macro base-stations subject to legacy users and RF-energy harvesting devices receiving a given data rate and amount of energy, respectively. It studies sub-carrier and transmit power allocation, and investigates III Abstract novel questions related to interference, which reduces network capacity but improves the amount of harvested energy by RF-energy harvesting devices. To study these questions, the problem is formulated as a Mixed-Integer Non Linear Program (MINLP). It contributes three linear approximations to the MINLP where devices are either assigned one or multiple sub-carriers. Numerical results show that RFenergy harvesting IoT devices will not affect network capacity if they can harvest sufficient energy from data transmissions to legacy devices. In addition, if multiple sub-carriers can be assigned to devices, the results show that the sum transmit power decreases by approximately 15% as compared to assigning a single sub-carrier to these devices. Second, this thesis considers an OFDMA based multi-cell environment. In particular, it considers multiple small cells that co-exist in a small coverage area. The problem of interest is sub-band(s) allocation to legacy data users and transmit power allocation at base stations. The problem is formulated as a MINLP. This thesis also presents two heuristics to assign a sub-band to base stations. Numerical results show that RF-energy harvesting devices will not affect network capacity if legacy data users require a high data rate. In addition, the results obtained from the two proposed heuristics are approximately 95% that of the optimal solution. Finally, this thesis considers a multi-objective resource allocation problem. Specifically, it aims to jointly maximize the sum-throughput of legacy devices and harvested energy of RF-energy harvesting devices. To do this, it considers a problem that aims to optimize sub-band(s) allocation to each base station and transmit power allocation over assigned sub-band(s). The problem is formulated as a MINLP. This problem is solved via a two layer approach. At the first layer, it employs a cross entropy based iterative solution to assign sub-band(s) to each base station. Given the assigned sub-band(s), at the second layer, the resulting MINLP becomes a nonlinear program, which can then be solved for the optimal transmit power. Moreover, an iterative heuristic is proposed to compare the results with that of the CE method. The simulation results show that transmit power control plays an important role to IV Abstract achieve the optimal solution by assigning all available sub-bands to each base station. Lastly, the cross-entropy method is capable of producing near optimal sub-band assignments. The results show that the number of iterations required to achieve the optimal solution decreases in proportion to the learning rate and percentile used to identify elite samples. V First of all, I would like to express my sincere gratitude to my principal supervisor, Associate Professor Kwan-Wu Chin, for his consistent support, guidance, encouragement, and patience throughout this project. I have been extremely fortunate to have had a supervisor who spent a lot of his precious time with me to improve my research skills. Despite his busy schedule, he always responded to my queries in a short time and always provided thoughtful comments. I respect his requirements of high quality from my research, and find that meeting this has driven me to finish this project to the best of my abilities. I would also like to express my gratitude to my co-supervisor, A/Prof Raad Raad, for his sincere support and contribution. I also want to thank all the anonymous reviewers and editors of conferences and journals for their time, effort and comments. Their suggestions have certainly raised the quality of my publications. A special thanks goes to my batch-mates, Mr Usman, Ms Maryam and Ms Yasmeen, with whom I started this journey. They have been a constant source of motivation for me. I am really grateful to my friend Dr. Saadullah Kalwar, for his support and encouragement in hard times.
RF Energy Harvesting for Wireless Devices
Radio Frequency (RF) energy transfer and harvesting techniques have recently become alternative methods to empower 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 requirements. In this paper, some wireless power transfer methods, RF energy harvesting networks, various receiver architectures and existing applications are presented. Finally, some open research directions are envisioned.