Energy harvesting aided localization for green cognitive radio network

Greshma, V; Sudha, T.

doi:10.1109/itcosp.2017.8303108

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Simulations are carried out with MATLAB R2017a and in this section numerical simulation results are presented to evaluate the EE of SCSTN with the proposed optimal methods. The simulation parameters are shown in Tab.2 unless otherwise stated [44][45][46]. When the ε of each satellite terminals are given by the predetermined p f , Fig.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

Energy-Efficient Cooperative Spectrum Sensing in Cognitive Satellite Terrestrial Networks

Bian

et al. 2020

IEEE Access

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Having the ability to provide seamless coverage and alleviate the frequency scarcity, the cognitive satellite terrestrial network becomes a promising candidate for future communication networks. In the cognitive network, spectrum sensing plays an important role in detecting the channel state for opportunistic utilization, where cooperative spectrum sensing is employed to improve the sensing performance. Additionally, it is critical for battery-powered satellite mobile terminals to diminish energy consumption costs. In this regard, this paper proposes a novel sensing-based cognitive satellite terrestrial network (SCSTN), which integrates the cognitive satellite terrestrial network with the distributed cooperative spectrum sensing network. Specifically, we focus on energy-efficient cooperative sensing in the SCSTN, which maximizes the energy efficiency (EE) of the cognitive satellite network by a tradeoff between the average throughput and the average energy consumption. In the SCSTN, the energy detection threshold of the sensing node and the rule threshold of fusion affect the average throughput and the average energy consumption. Hence, the objective of this paper is to identify the energy detection threshold of the sensing node and the rule threshold of fusion to achieve the maximum EE. We first study the EE formulation of the rule threshold of fusion when the energy detection threshold of the sensing node is given, and transform the ratio-type objective function of EE into a parametric formulation. Subsequently, by exploring the relationship between the two formulations and making use of the monotonicity of the parametric formulation, an algorithm to obtain the optimal rule threshold of fusion for the original problem is developed. Furthermore, we study the optimal formulation of the energy sensing threshold of the sensing node and discuss the effect of the sensing duration and the number of distributed cooperative terminals on the EE. Lastly, the performance of the proposed method is evaluated through numerical simulation results.

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Section: Simulation Results and Analysismentioning

confidence: 99%

Energy-Efficient Cooperative Spectrum Sensing in Cognitive Satellite Terrestrial Networks

Bian

et al. 2020

IEEE Access

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Performance Analysis of Cognitive Radio Network for Opportunistic Wireless Energy Harvesting

Qin

Zhang

Ren

2019

2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

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Optimal resource allocation method for energy harvesting based underlay Cognitive Radio networks

Liao

Jiang

et al. 2023

PLoS ONE

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This paper proposes an optimal resource allocation method. The method is to maximize the Energy Efficiency (EE) for an Energy Harvesting (EH) enabled underlay Cognitive Radio (CR) network. First, we assumed the Secondary Users (SUs) can harvest energy from the surrounding Radio Frequency (RF) signals. Then, we modelled the EE maximisation problem as a joint time and power optimization model. Next, the optimal EH time allocation factor can be calculated. After that the optimal power allocation strategy can be obtain by the fractional programming and Lagrange multiplier method. Finally simulation results show that the proposed iterative method can be better performance advantages compared with the exhaustive method and genetic algorithm. And the EE of this system model is significantly improved compared to the EE model without considering EH.

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Energy harvesting aided localization for green cognitive radio network

Cited by 3 publications

References 14 publications

Energy-Efficient Cooperative Spectrum Sensing in Cognitive Satellite Terrestrial Networks

Energy-Efficient Cooperative Spectrum Sensing in Cognitive Satellite Terrestrial Networks

Performance Analysis of Cognitive Radio Network for Opportunistic Wireless Energy Harvesting

Optimal resource allocation method for energy harvesting based underlay Cognitive Radio networks

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