Probability of Detection Analysis in Fading and Nonfading Scenario Using Cooperative Sensing Technique

Sharma, Meenakshi; Chauhan, Prakash; Sarma, Nityananda

doi:10.1007/978-981-10-6890-4_18

Cited by 3 publications

(4 citation statements)

References 7 publications

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“…The findings are sent to the FC, where final decisions are made by the CH [45]. Individual spectrum sensing clustering performance metrics are also used for CSS [47].Due to the analog to digital converter requirement, Nyquist sampling rate is not valuable for wideband spectrum sensing. So the author in [50] reviews wideband spectrum sensing based on Nyquist sampling.…”

Section: Theoretical Backgroundmentioning

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: Theoretical Backgroundmentioning

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

“…The findings are sent to the FC, where final decisions are made by the CH 45 . Individual spectrum sensing clustering performance metrics are also used for CSS 47 .Due to the analog to digital converter requirement, Nyquist sampling rate is not valuable for wideband spectrum sensing. So the author in 50 reviews wideband spectrum sensing based on Nyquist sampling.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

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.

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“…When the number of cooperating SUs is very high, these schemes become more complex with high processing time, which limits their efficiency. Thus, a cooperative spectrum sensing based clustering solution was proposed in [26][27][28][29][30][31][32]. SUs are combined in clusters based on some features such as the geographic area and the distance to the PU.…”

Section: Cooperative Spectrum Sensing Based Clusteringmentioning

confidence: 99%

“…Figure 1 represents an example of a cluster based cooperative spectrum sensing with 6 SUs divided into 3 clusters. The performance evaluation metrics used for cooperative spectrum sensing based clustering are also derived from the performance evaluation metrics for individual spectrum sensing [30]. The cooperative probability of detection based clustering of the k th cluster, Qd,k, is expressed as…”

Section: Cooperative Spectrum Sensing Based Clusteringmentioning

confidence: 99%

A Cooperative Spectrum Sensing Scheme Based on Compressive Sensing for Cognitive Radio Networks

Salahdine¹,

Ghribi²,

Kaabouch³

2019

IJDIWC

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In this paper, a cooperative spectrum sensing scheme based on compressive sensing is proposed. In this scheme, secondary users (SUs) are organized in clusters. In each cluster, SUs forward their compressed signals to the cluster head. Then, the cluster heads report the local sensing data to the fusion center to make the final decision. Analog to information converter is adopted to acquire the received SUs signals. The proposed scheme is evaluated using several metrics, including probability of detection, compression ratio, probability of error, and processing time. The simulation results are compared with those of other cooperative sensing schemes. With our proposed scheme, cooperative spectrum sensing achieves a high probability of detection of 97%, a processing time of 2ms, a low compression ratio of 10%, and low error rate of 35 %. These results show that our proposed method outperforms the spectrum sensing without compressive sensing.

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Probability of Detection Analysis in Fading and Nonfading Scenario Using Cooperative Sensing Technique

Cited by 3 publications

References 7 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

A Cooperative Spectrum Sensing Scheme Based on Compressive Sensing for Cognitive Radio Networks

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