A novel double threshold-based spectrum sensing technique at low SNR under noise uncertainty for Cognitive Radio Systems

Mahendru, Garima

doi:10.21203/rs.3.rs-397280/v1

Cited by 2 publications

(1 citation statement)

References 36 publications

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“…A CRN suggested the greedy algorithm and particle swarm optimization to improve spectrum sensing [18]. In [19], mathematical modelling and critical assessment of detection probability for energy detection-based spectrum sensing at low SNR in an uncertain, noisy environment are presented. A mathematical model has been suggested to calculate two thresholds for reliable sensing when measured energy is smaller than noise power uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Performance Enhancement in Clustering Cooperative Spectrum Sensing for Cognitive Radio Network Using Metaheuristic Algorithm

Srivast

Singh

Mahajan

et al. 2023

Preprint

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Spectrum sensing describes, whether the spectrum is occupied or empty. Main objective of cognitive radio network (CRN) is to increase probability of detection (Pd) and reduce probability of error (Pe) for energy consumption. To reduce energy consumption, probability of detection should be increased. In cooperative spectrum sensing (CSS), all secondary users (SU) transmit their data to fusion center (FC) for final measurement according to the status of primary user (PU). Cluster should be used to overcome this problem and improve performance. In the clustering technique, all SUs are grouped into clusters on the basis of their similarity. In cluster technique, SU transfers their data to cluster head (CH) and CH transfers their combined data to FC. This paper proposes the detection performance optimization of CRN with a machine learning-based metaheuristic algorithm using clustering CSS technique. This article presents a hybrid support vector machine (SVM) and Red Deer Algorithm (RDA) algorithm named Hybrid SVM-RDA to identify spectrum gaps. Algorithm proposed in this work outperforms the computational complexity, an issue reported with various conventional cluster techniques. The proposed algorithm increases the probability of detection (up to 99%) and decreases the probability of error (up to 1%) at different parameters.

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Section: Introductionmentioning

confidence: 99%

Performance Enhancement in Clustering Cooperative Spectrum Sensing for Cognitive Radio Network Using Metaheuristic Algorithm

Srivast

Singh

Mahajan

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Performance enhancement in clustering cooperative spectrum sensing for cognitive radio network using metaheuristic algorithm

Srivastava,

Singh,

Mahajan

et al. 2023

Sci Rep

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Spectrum sensing describes, whether the spectrum is occupied or empty. Main objective of cognitive radio network (CRN) is to increase probability of detection (Pd) and reduce probability of error (Pe) for energy consumption. To reduce energy consumption, probability of detection should be increased. In cooperative spectrum sensing (CSS), all secondary users (SU) transmit their data to fusion center (FC) for final measurement according to the status of primary user (PU). Cluster should be used to overcome this problem and improve performance. In the clustering technique, all SUs are grouped into clusters on the basis of their similarity. In cluster technique, SU transfers their data to cluster head (CH) and CH transfers their combined data to FC. This paper proposes the detection performance optimization of CRN with a machine learning-based metaheuristic algorithm using clustering CSS technique. This article presents a hybrid support vector machine (SVM) and Red Deer Algorithm (RDA) algorithm named Hybrid SVM–RDA to identify spectrum gaps. Algorithm proposed in this work outperforms the computational complexity, an issue reported with various conventional cluster techniques. The proposed algorithm increases the probability of detection (up to 99%) and decreases the probability of error (up to 1%) at different parameters.

show abstract

A novel double threshold-based spectrum sensing technique at low SNR under noise uncertainty for Cognitive Radio Systems

Cited by 2 publications

References 36 publications

Performance Enhancement in Clustering Cooperative Spectrum Sensing for Cognitive Radio Network Using Metaheuristic Algorithm

Performance Enhancement in Clustering Cooperative Spectrum Sensing for Cognitive Radio Network Using Metaheuristic Algorithm

Performance enhancement in clustering cooperative spectrum sensing for cognitive radio network using metaheuristic algorithm

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