Designing an Optimal Energy Efficient Cluster-Based Spectrum Sensing for Cognitive Radio Networks

Awin, Faroq; Abdel-Raheem, Esam; Ahmadi, Majid

doi:10.1109/lcomm.2016.2585126

Cited by 36 publications

(29 citation statements)

References 8 publications

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“…At cluster level, as a first step, each CH estimates its SNR and forwards it to the FC to compute the threshold,γ c . Second step, each CH compares its SNR, γ c (i), with the threshold; if its greater than it, then the CH will employ one-bit fusion rule and its probability of detection is computed using Equation (8). Otherwise, the CH will employ two-bit fusion rule and its probability of detection is computed using Equation (11).…”

Section: Simple Combined Algorithmmentioning

confidence: 99%

“…The first step of the adaptive combined algorithm is to set the initial value of the maximum allowable global probability of false alarm,Q f c , and the adaptive threshold ϕ. TheQ f c is set as either 0.05 or 0.005 which the maximum expected probability of false alarm over a cluster, while the adaptive threshold, ϕ, is set as the average of probabilities of all clusters in the CRN, when all clusters employ one-bit fusion rule. In other words, each cluster computes its probability of detection using Equation (8).…”

Section: Adaptive Combined Algorithmmentioning

confidence: 99%

“…These schemes mainly focus on improving the detection performance, i.e., probability of detection and/or throughput. For instance, optimizing the fusion rule employed in a CRN [7][8][9], optimizing the sensing time [10,11] fall in this class.…”

Section: Introductionmentioning

confidence: 99%

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Combined Fusion Rules in Cognitive Radio Networks Using Different Threshold Strategies

2019

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Compromising the performance and overhead is a crucial factor in designing cognitive radio networks (CRNs). One way to achieve this goal is to combine different fusion rules for a CRN with multiple clusters of cognitive radios (CRs). This paper proposes a new adaptive combination algorithm to balance between detection performance of a CRN and its reporting overhead through combining different fusion rules over the CRN. Initially, the paper describes how to combine hard decision, i.e., one-bit, and soften-hard decision, i.e., two-bit, fusion rules over a CRN with multiple clusters of CRs using different strategies. Simple combination and modified combination strategies, to consider a trade off between performance improvement and incurred reporting overhead, are considered. The paper adopts different threshold strategies to implement the proposed combinations. Moreover, the proposed algorithms are examined under the Rayleigh fading channel model and simulated to investigate their detection performance and to compare their detection performance with existing works. The simulation results show that the adaptive threshold strategy outperforms the two proposed fixed threshold strategies and conventional fusion schemes.

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Section: Simple Combined Algorithmmentioning

confidence: 99%

Section: Adaptive Combined Algorithmmentioning

confidence: 99%

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Combined Fusion Rules in Cognitive Radio Networks Using Different Threshold Strategies

2019

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“…To prolong the lifetime of cognitive sensor network (CSN) and overcome the energy limitation of the CSs, energy harvesting technology is used to provide sufficient energy to fuel the CSN. Moreover, energy-efficient designs [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ] and energy harvesting [ 8 , 9 , 10 , 11 , 12 ] represent the two important directions for solving the energy limitation for CSs. Energy-efficient designs can be classified into four groups [ 13 ]: minimizing energy consumption, optimizing sensing parameters (e.g., the number of cooperating nodes, detection thresholds, sensing and transmission duration, transmission power), selection and application of fusion and decision rules, and energy-efficient network organization.…”

Section: Introductionmentioning

confidence: 99%

“…According to this master of literature, only a small amount have taken the case mentioned above into account in designing the energy-efficient spectrum sensing scheme. The authors in [ 4 ] considered a framework to jointly optimize design parameters (sensing duration, transmission duration, and the number of cognitive users) that maximized the energy-efficiency. On the basis of the [ 4 ], the authors in [ 5 ] optimized transmission power and sensing time to maximize energy efficiency and proposed an iterative algorithm to reduce the complexity of solving the optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Optimal Time Assignment Policy for Maximizing Throughput in Cognitive Sensor Network with Energy Harvesting

Chen

2018

Sensors

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A cognitive sensor network with energy harvesting (EH-CSN) is a promising paradigm to address the issues both in spectrum efficiency and in energy efficiency. The cognitive sensors (CSs) equipped with energy harvesting devices are assumed to operate in a harvesting-sensing-transmission mode and permitted to access the idle licensed frequency bands without causing any harmful jamming to the primary user. By identifying the time fractions of harvesting, sensing, and transmission, we can discuss some design considerations for the EH-CSN. In the meantime, considering the possibility that the primary user may reoccupy the idle channel during the CS’s data transmission duration, we formulate an optimization problem to maximize the average throughput of EH-CSN under a collision constraint and an energy constraint. After deriving the lower and upper bounds of the time fraction for energy harvesting, the uniqueness and existence of the optimal time fraction set have been proved. Finally, our theoretical analysis is also verified through numerical simulations.

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Optimal selection based K‐mean clustering technique to improve the energy efficiency in cognitive radio networks for 6G applications

Rajavel¹,

Aruna²,

Rajakumar³

2021

Int J Communication

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The main objective of the cognitive radio sensor network (CRSN) is to minimize the energy consumption in channel sensing and switching when sensor nodes sense and switch to a licensed channel for improving the energy efficiency for 6G applications. The drawback in CRSN is that the sensor network uses a higher amount of energy by intrabunch and interbunch of data emission energetic medium ingression assignment problem when the sensor nodes sense the channel. In this brief, a K-mean clustering technique (KMCT) is proposed for CRSN to minimize the energy used by the sensor nodes in CRSN for 6G applications. The KMCT-based joint power medium ingression technique (JPMIT) is introduced for enhancing the energy efficiency in a bunch of interdata emissions, and the KMCT-based energetic medium ingression technique (EMIT) is introduced for minimizing the energy utilization in a bunch of intradata emissions. Simulation results show that the proposed KMCT for CRSN provides a better result compared with existing techniques. The performance parameters such as medium available duration, data loss rate, quantity of data, number of authorized mediums, and probability of false alarm have been analyzed using MATLAB R2012a.

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Designing an Optimal Energy Efficient Cluster-Based Spectrum Sensing for Cognitive Radio Networks

Cited by 36 publications

References 8 publications

Combined Fusion Rules in Cognitive Radio Networks Using Different Threshold Strategies

Combined Fusion Rules in Cognitive Radio Networks Using Different Threshold Strategies

Optimal Time Assignment Policy for Maximizing Throughput in Cognitive Sensor Network with Energy Harvesting

Optimal selection based K‐mean clustering technique to improve the energy efficiency in cognitive radio networks for 6G applications

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