Multi-Band RF Energy and Spectrum Harvesting in Cognitive Radio Networks

Alsharoa, Ahmad; Neihart, Nathan M.; Kim, Sang Woon; Kamal, Ahmed E.

doi:10.1109/icc.2018.8422511

Cited by 17 publications

(33 citation statements)

References 21 publications

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“…Also two cognitive modes: overlay mode and underlay mode are proposed to enhance the performance of architecture. The researchers in [30] propose an efficient region based multichannel sensing. A winwin architecture is designed according to which a secondary user is allowed to sense the spectrum.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Analysis of Multi-Channel CR Enabled IoT Network with Better Energy Harvesting

Mahmood¹,

Khan²

2022

Computers, Materials &Amp; Continua

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Wireless Sensor Networks (WSNs) can be termed as an autoconfigured and infrastructure-less wireless networks to monitor physical or environmental conditions, such as temperature, sound, vibration, pressure and motion etc. WSNs may comprise thousands of Internet of Things (IoT) devices to sense and collect data from its surrounding, process the data and take an automated and mechanized decision. On the other side the proliferation of these devices will soon cause radio spectrum shortage. So, to facilitate these networks, we integrate Cognitive Radio (CR) functionality in these networks. CR can sense the unutilized spectrum of licensed users and then use these empty bands when required. In order to keep the IoT nodes functional all time, continuous energy is required. For this reason the energy harvested techniques are preferred in IoT networks. Mainly it is preferred to harvest Radio Frequency (RF) energy in the network. In this paper a region based multi-channel architecture is proposed. In which the coverage area of primary node is divided as Energy Harvesting Region and Communication Region. The Secondary User (SU) that are the licensed user is IoT enabled with Cognitive Radio (CR) techniques so we call it CR-enabled IoT node/device and is encouraged to harvest energy by utilizing radio frequency energy. To harvest energy efficiently and to reduce the energy consumption during sensing, the concept of overlapping region is given that supports to sense multiple channels simultaneously and help the SU to find best channel for transmitting data or to harvest energy from the ideal channel. From the experimental analysis, it is proved that SU can harvest more energy in overlapping region and this architecture proves to consume less energy during data transmission as compared to single channel. We also show that channel load can be highly reduced and channel utilization is proved to be more proficient. Thus, this proves the proposed architecture cost-effective and energy-efficient.

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Section: Literature Reviewmentioning

confidence: 99%

Performance Analysis of Multi-Channel CR Enabled IoT Network with Better Energy Harvesting

Mahmood¹,

Khan²

2022

Computers, Materials &Amp; Continua

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“…The hardware aspects of a platform consist of the Radio-Frequency (RF) elements, some baseband signal processing and communications link to the software based signal processing element-perhaps a DSP, FPGA, or a general purpose processor (GPP) (Figure 3). According to functions performed by an SDR, it can be called to be: i. Multiband system: Multiband system reuse the locally unused spectrum and increase the total system capacity [4,5]. For implementing multiband system CR will listen to the channel and, if the channel is free it will transmit.…”

Section: Front-end Issues Of Sdrmentioning

confidence: 99%

Cognitive Radio-Modulation and Demodulation

Gummineni¹,

Polipalli²

2020

Recent Trends in Communication Networks

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The reconfigurability in Cognitive Radio (CR) facilitates to dynamically change its parameters for the efficient spectrum utilization. The motivation behind the study of cognitive radio is that the number of different radio signals can be handled without using extra circuitry, i.e., reusing identical hardware with the change in the software will reduce time to market, development cost, and upgrade infrastructure. Software Defined Radio (SDR) is an enabling technology for Cognitive Radio (CR); therefore, it emphasizes on SDR unique features, characteristics, and basics concepts that are required to understand operation of SDR. SDR allows service providers to upgrade infrastructure without unreasonable cost. Modulation techniques play a vital role in any communication systems such as cable modems, DSL modems, CDMA, 4G, Wi-Fi, and WIMAX; thus, it emphasizes on implementation of modulation techniques using SDR Generic hardware, which is operated by Open Source software called GNU Radio. Implementation of various analog and digital modulation techniques using the GNU Radio provides a way for developing advanced wireless communication system. GNU Radio software is a highly flexible signal processing platform, which makes it easy and reduces time to implement different modulation techniques with appropriate script.

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“…An SU requiring to transmit data would need to stay in at least one of harvesting zones of active PUs, otherwise the SU will have no energy for transmitting data. In [24] the problem is formulated to jointly optimize the number of sensing samples and sensing threshold in order to minimize the sensing time and hence maximize the harvested energy. Xu et al [25] investigated the problem to determine the optimal channel selection probability that maximizes the average throughput of SUs.…”

Section: A Related Workmentioning

confidence: 99%

Cooperative Spectrum Sensing in Multichannel Cognitive Radio Networks With Energy Harvesting

2019

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This paper investigates cooperative spectrum sensing in multi-channel cognitive radio networks (CRNs) with energy harvesting. Our goal is two-fold: first, to determine the optimal sensing parameters for effective management of the limited energy budget in order to maximize the achievable throughput, and second, to exploit the benefits of a practical CRN towards improving the performance of the energy constrained CRN. Two different scenarios are considered. In the first, the secondary user (SU) is assigned a single radio frequency (RF) harvesting source, while in the second, the SU is assigned multiple RF harvesting sources and can opportunistically harvest from any of the sources. For these scenarios, the problem is formulated as a stochastic optimal control system with infinite and continuous state and action spaces. This is known to be computationally intractable and becomes even more complicated in a two-dimensional problem such as considered. In order to reduce the computational complexity, a myopic optimization approach is taken, and the problem is formulated into a mixed integer nonlinear problem (MINLP) to determine the channel assignment, the sensing duration, the distribution of the sensing duration associated with the assigned channels and the detection threshold under the constraint of energy causality and primary user (PU) protection. A near-optimal solution is obtained to the MINLP based on the alternating convex optimization technique. The simulation results obtained show that the considered work can improve the amount of energy harvested and, by extension, the active probability of the SUs by exploiting the multi-channel benefits of practical CRN for enhanced throughput.INDEX TERMS Energy harvesting cognitive radio network (EH-CRN), cooperative spectrum sensing, energy harvesting sources, primary user (PU), multi-channel, optimization problem.

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Multi-Band RF Energy and Spectrum Harvesting in Cognitive Radio Networks

Cited by 17 publications

References 21 publications

Performance Analysis of Multi-Channel CR Enabled IoT Network with Better Energy Harvesting

Performance Analysis of Multi-Channel CR Enabled IoT Network with Better Energy Harvesting

Cognitive Radio-Modulation and Demodulation

Cooperative Spectrum Sensing in Multichannel Cognitive Radio Networks With Energy Harvesting

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