Cognitive Radio Sensor Network with Green Power Beacon (original) (raw)
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On Green-Energy-Powered Cognitive Radio Networks
IEEE Communications Surveys & Tutorials, 2015
Green energy powered cognitive radio (CR) network is capable of liberating the wireless access networks from spectral and energy constraints. The limitation of the spectrum is alleviated by exploiting cognitive networking in which wireless nodes sense and utilize the spare spectrum for data communications, while dependence on the traditional unsustainable energy is assuaged by adopting energy harvesting (EH) through which green energy can be harnessed to power wireless networks. Green energy powered CR increases the network availability and thus extends emerging network applications. Designing green CR networks is challenging. It requires not only the optimization of dynamic spectrum access but also the optimal utilization of green energy. This paper surveys the energy efficient cognitive radio techniques and the optimization of green energy powered wireless networks. Existing works on energy aware spectrum sensing, management, and sharing are investigated in detail. The state of the art of the energy efficient CR based wireless access network is discussed in various aspects such as relay and cooperative radio and small cells. Envisioning green energy as an important energy resource in the future, network performance highly depends on the dynamics of the available spectrum and green energy. As compared with the traditional energy source, the arrival rate of green energy, which highly depends on the environment of the energy harvesters, is rather random and intermittent. To optimize and adapt the usage of green energy according to the opportunistic spectrum availability, we discuss research challenges in designing cognitive radio networks which are powered by energy harvesters.
Cognitive radio network with continuous energy-harvesting
International Journal of Communication Systems, 2016
In this paper, the performance of a cooperative cognitive radio (CR) network is investigated under continuous energy harvesting scenario. A CR node harvests energy from both the sources: non-radio frequency (RF) signal (ambient sources) or from RF signal (primary user signal). It harvests from non-RF signal during sensing time of its detection cycle, and from both the sources, RF signal and non-RF signal, during transmission time as per sensing decision. Several novel analytical expressions are developed to indicate the harvested energy, energy reward, energy cost in a detection frame, and throughput. The performance of the CR network is investigated to maximize the throughput considering energy causality constraints and collision constraints. Analytical results are validated through extensive simulation results.
RF-powered Green Cognitive Radio Networks: Architecture and Performance Analysis
IEEE Communications Letters, 2015
In this letter we consider an RF-powered Green Cognitive Radio Network (RF-GCRN), where a central node, called Power Beacon (PB), harvests green energy from ambient sources and wirelessly delivers random harvested energy to cognitive users. Random in-band energy transmission by PB is the only energy source of cognitive users. Performance of this network, with a single pair of secondary users, is analyzed under two spectrum access schemes, i.e., random access and spectrum sensing-based access schemes. Results show feasibility of RF-GCRN model, if the energy transmission rate is below a certain threshold. This threshold is determined according to maximum tolerable delay of primary user and parameters of spectrum access scheme. Finding a closed form expression for this threshold results in a quite complicated formula, which cannot be helpful in determining feasibility region, due to excessive complexity. Instead, we numerically calculated feasibility regions of both access schemes to facilitate parameter selection process.
Resource Allocation in Cognitive Radio Wireless Sensor Networks with Energy Harvesting
Sensors, 2019
The progress of science and technology and the expansion of the Internet of Things make the information transmission between communication infrastructure and wireless sensors become more and more convenient. For the power-limited wireless sensors, the life time can be extended through the energy-harvesting technique. Additionally, wireless sensors can use the unauthored spectrum resource to complete certain information transmission tasks based on cognitive radio. Harvesting enough energy from the environments, the wireless sensors, works as the second users (SUs) can lease spectrum resource from the primary user (PU) to finish their task and bring additional transmission cost to themselves. To minimize the overall cost of SUs and to maximize the spectrum profit of the PU during the information transmission period, we formulated a differential game model to solve the resource allocation problem in the cognitive radio wireless sensor networks with energy harvesting, considering the SU...
Throughput of a Cognitive Radio Network With Energy-Harvesting Based on Primary User Signal
IEEE Wireless Communications Letters, 2016
In this paper, we analyze an energy-harvesting based Cognitive Radio (CR) system. The CR system harvests energy from the radio frequency (RF) signal of primary user (PU) during sensing time as well as the transmission time of a detection cycle if PU is present. The CR accesses the spectrum band of PU opportunistically using the energy harvested over the frames with PU present, while maintaining a quality of service (QoS) constraint on PU in terms of a collision probability. An optimal sensing time is found which maximizes the harvested energy. The performance is investigated in terms of harvested energy, outage probability and throughput of the network. Novel analytical expressions for average harvested energy and average throughput are developed under such a scenario which are validated by simulation.
2022
Background and Objectives: In an energy harvesting cognitive radio network, both energy efficiency and spectrum efficiency can be improved, simultaneously. In this paper, we consider an energy harvesting-based multiantenna cognitive radio network to execute cooperative spectrum sensing, data transmission and RF energy harvesting by secondary transmitter from PU' signal and the ambient noise, simultaneously. Methods: In his paper, two novel models called Joint Power allocation and Energy Harvesting by Time switching and Antennas splitting (JPEHTA) and Joint Power allocation and Continuous Energy Harvesting (JPCEH) are proposed. We formulate the joint optimization problems of the sensing time, detection threshold, energy harvesting time, number of cooperative antennas for sensing and energy harvesting as well as power allocation for each antenna in both proposed models. The aim is for enhancing both the spectral and the energy efficiencies under constraints on the probabilities of global detection and false alarm, energy harvesting and transmission power budget. Then, the considered multi-variable problem is solved by using two convex-based iterative proposed algorithms having less computational complexity compared to baseline approaches to achieve the optimal parameters and goals of the problem. Results: The results present insights about the impact of the sensing time, detection threshold, power allocation and the number of antennas on the energy and spectrum efficiencies of cognitive radio network with an energy harvesting capability. Conclusion: Simulation results have shown that the proposed schemes outperform the structures that have not optimized all the parameters considered in this paper, jointly or schemes in which single-antenna SU are participated in spectrum sensing, energy harvesting and data transmitting.
SURVEY ON ENERGY HARVESTING COGNITIVE RADIO NETWORK
Energy harvesting network (EHN) is a trending topic among the recent researches. This substantial attention is due to the limitations, operational cost and risks of the conventional power suppliers, such as fossil fuel and batteries. Moreover, EHN are expected to enhance energy efficiency by harvesting energy of RF and renewable sources. In contemporary research works, EHN is applied to CR technology. This energy harvesting cognitive radio network (EH-CRN) is expected to utilize both energy and electromagnetic spectrum efficiently. However, EH-CRN is facing enormous challenges related to technical design. Some of these challenges are reviewed in recent surveys. However, other challenges such as optimizing the network throughput and EH-CRN implementation models were not the focus of these researches. Therefore, the aim of this survey is to review EH-CRN research works by focusing the survey perspective on maximizing the network throughput and the implementation models.
Survey on energy harvesting cognitive radio networks
2015
Energy harvesting network (EHN) is a trending topic among the recent researches. This substantial attention is due to the limitations, operational cost and risks of the conventional power suppliers, such as fossil fuel and batteries. Moreover, EHN are expected to enhance energy efficiency by harvesting energy of RF and renewable sources. In contemporary research works, EHN is applied to CR technology. This energy harvesting cognitive radio network (EHCRN) is expected to utilize both energy and electromagnetic spectrum efficiently. However, EH-CRN is facing enormous challenges related to technical design. Some of these challenges are reviewed in recent surveys. However, other challenges such as optimizing the network throughput and EH-CRN implementation models were not the focus of these researches. Therefore, the aim of this survey is to review EH-CRN research works by focusing the survey perspective on maximizing the network throughput and the implementation models.
Design an Optimum Energy Harvesting Model for Bidirectional Cognitive Radio Networks
2021
The energy efficiency and spectrum shortage problem of wireless devices has become a concern for researchers worldwide as the number of wireless devices increases at an unparalleled speed. Many new solutions have been proposed to extend mobile devices' battery life, such as wireless energy harvesting from traditional radio frequency signals to design new smart battery chips. This paper considers a cognitive radio network model where primary users have their specific licensed band, and secondary users equipped with necessary hardware required for energy harvesting can use the licensed band of the primary user by smart sensing capability. First, the expression of outage probability is theoretically derived for uplink and downlink scenarios. Moreover, maximum energy efficiency for both uplink and downlink in the cognitive radio network model subject to interference and noise is investigated here. The theoretical analysis is then evaluated. It has been observed that outage probabili...
Throughput Maximization for Sensor-Aided Cognitive Radio Networks with Continuous Energy Arrivals
Sensors, 2015
We consider a Sensor-Aided Cognitive Radio Network (SACRN) in which sensors capable of harvesting energy are distributed throughout the network to support secondary transmitters for sensing licensed channels in order to improve both energy and spectral efficiency. Harvesting ambient energy is one of the most promising solutions to mitigate energy deficiency, prolong device lifetime, and partly reduce the battery size of devices. So far, many works related to SACRN have considered single secondary users capable of harvesting energy in whole slot as well as short-term throughput. In the paper, we consider two types of energy harvesting sensor nodes (EHSN): Type-I sensor nodes will harvest ambient energy in whole slot duration, whereas type-II sensor nodes will only harvest energy after carrying out spectrum sensing. In the paper, we also investigate long-term throughput in the scheduling window, and formulate the throughput maximization problem by considering energy-neutral operation conditions of type-I and-II sensors and the target detection probability. Through simulations, it is shown that the sensing energy consumption of all sensor nodes can be efficiently managed with the proposed scheme to achieve optimal long-term throughput in the window.