Energy provision minimization of energy-harvesting cognitive radio networks with minimal throughput demands

Zheng, Kechen; Ge, Haijiang; Chi, Kaikai; Liu, Xiaoying

doi:10.1016/j.comnet.2021.108721

Cited by 8 publications

(5 citation statements)

References 33 publications

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“…Energy harvesting and cognitive radio technologies design deep-learning-based IoT networks [ 81 ]: spectrally and energy-efficient transmission schemes should be articulated in large-scale connection and device support. Spectrum reutilization and lifetime extension assist energy-harvesting cognitive radio networks [ 82 ]: the energy provision of a primary transmitter can be reduced while meeting the requirements of minimal-throughput networks and users.…”

Section: Energy-harvesting Technology For Cognitive-radio-based Iot N...mentioning

confidence: 99%

Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based Internet-of-Things Networks

Fernando,

Lăzăroiu

2023

Sensors

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The aim of this systematic review was to identify the correlations between spectrum sensing, clustering algorithms, and energy-harvesting technology for cognitive-radio-based internet of things (IoT) networks in terms of deep-learning-based, nonorthogonal, multiple-access techniques. The search results and screening procedures were configured with the use of a web-based Shiny app in the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) flow design. AMSTAR, DistillerSR, Eppi-Reviewer, PICO Portal, Rayyan, and ROBIS were the review software systems harnessed for screening and quality assessment, while bibliometric mapping (dimensions) and layout algorithms (VOSviewer) configured data visualization and analysis. Cognitive radio is pivotal in the utilization of an adequate radio spectrum source, with spectrum sensing optimizing cognitive radio network operations, opportunistic spectrum access and sensing able to boost the efficiency of cognitive radio networks, and cooperative spectrum sharing together with simultaneous wireless information and power transfer able increase spectrum and energy efficiency in 6G wireless communication networks and across IoT devices for efficient data exchange.

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Section: Energy-harvesting Technology For Cognitive-radio-based Iot N...mentioning

confidence: 99%

Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based Internet-of-Things Networks

Fernando,

Lăzăroiu

2023

Sensors

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“…Según la Comisión Federal de Comunicaciones, el espectro está gravemente infrautilizado (Zheng et al, 2022), se estima que el 80 % del espectro asignado está por debajo del uso potencial, esto significa que la disponibilidad de frecuencias para los usuarios actuales y los nuevos usuarios será un problema desafiante (Gupta y Kumar, 2022). Para resolver este problema se han propuesto diferentes estrategias, la CR es una tecnología que ha recibido una gran atención por parte de la academia y la industria, permite realizar la asignación del espectro de forma dinámica, está compuesta por usuarios licenciados o primarios (Primary User -PU) y usuarios sin licencia o secundarios (Secondary User -SU).…”

Section: Introductionunclassified

Clasificación de la ocupación espectral para la toma de decisiones en redes inalámbricas cognitivas implementando extracción de características y aprendizaje automático

2022

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La radio cognitiva (CR) es una tecnología que busca maximizar el uso de los recursos de frecuencia al permitir que los usuarios sin licencia accedan a las bandas de espectro de forma oportunista. Se analiza el proceso de toma de decisiones a través de la clasificación de tres niveles de tráfico. Para evaluar el proceso, se utilizan tres métricas de costo y una de beneficio. Los resultados muestran que el clasificador que utiliza máquinas de soporte vectorial presenta el mejor desempeño, seguido por KnNC (K-nearest Neighbor Classifier) y DAC (Discriminan Analysis Classifier). El desempeño más bajo, con los indicadores de desempeño más deficiente, lo obtiene BDT (Binary Decision Tree). Se concluye que la CR ofrece un conjunto de soluciones al hacer que el espectro se use de manera dinámica.

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“…Currently, only a small amount of research has been carried out on the problems concerning the throughput demand as follows. Zheng et al [3] minimized the primary transmitter (PT)'s energy provision (EP) while satisfying the network's or users' minimal throughput demands. Wang et al [17] focused on the transmission power allocation to maximize the average throughput of SUs under the minimum throughput constraint of each SU.…”

Section: Introductionmentioning

confidence: 99%

“…In order to improve the spectrum efficiency (SE) as well as address the spectrum scarcity problem, cognitive radio (CR) has been proposed as a promising technology, which allows secondary users (SUs) to dynamically access licensed spectrum under the condition of protecting primary transmission [2]. Along with the concern about spectrum reuse, the technology of energy harvesting in wireless networks has attracted great concern on energy efficiency, and provides permanent energy supply without manual battery recharging and replacement [3]. In addition, energy harvesting is a promising technique, which can scavenge energy from the renewable sources [4], such as solar [5], wind [6], radio frequency signals [7] etc.…”

Section: Introductionmentioning

confidence: 99%

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The maximal secondary throughput in cognitive radio networks with energy harvesting

Zheng

Chi

et al. 2022

IET Communications

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In terms of spectrum reuse and lifetime prolongation, the energy‐harvesting cognitive radio networks (EH‐CRNs) have been a hot issue in the wireless networking research community. While satisfying the minimal throughput demand of primary users (PUs), it is aimed to maximize the throughput of secondary users (SUs) in the EH‐CRN with multiple SUs. Specifically, the problem of secondary throughput maximization (STM) is first formulated as a non‐linear optimization problem, then its convexity is proven, and finally an efficient algorithm is proposed that jointly uses the Fibonacci search method and equal interval search method to obtain the optimal time allocation among primary transmitter (PT)'s energy transfer and each SU's packet transmission, and the optimal transmit power of PT. Furthermore, for the scenarios where the circuit power is negligible, the convex problem is first proven, and a more efficient algorithm is presented for the problem of STM. Simulation results demonstrate that, with the increase of the minimal throughput demand of PUs, the reduction rate of the maximal secondary throughput increases.

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Energy provision minimization of energy-harvesting cognitive radio networks with minimal throughput demands

Cited by 8 publications

References 33 publications

Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based Internet-of-Things Networks

Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based Internet-of-Things Networks

Clasificación de la ocupación espectral para la toma de decisiones en redes inalámbricas cognitivas implementando extracción de características y aprendizaje automático

The maximal secondary throughput in cognitive radio networks with energy harvesting

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