Enhancing the Spectrum Sensing Performance of Cluster-Based Cooperative Cognitive Radio Networks via Sequential Multiple Reporting Channels

Hossain, Mohammad Amzad; Schukat, Michael; Barrett, Enda

doi:10.1007/s11277-020-07802-4

Cited by 18 publications

(8 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The disadvantage is a low probability of detection and a high probability of error. In [39], multiple reporting channels (MRC) for cluster-based cooperative CRN are proposed, which allows for greater use of the reporting time frame by increasing the sensing time of SU, thus reducing the overall cost.…”

Section: Related Work and Problem Formulationmentioning

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

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

Section: Related Work and Problem Formulationmentioning

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

“…To effectively use the report time slot by increasing the detecting time of SUs, in 2021, Hossain et al [ 30 ] suggested the idea of Multiple Reporting Channels (MRCs) for clustered CRNs. In this method, each cluster was given a reporting channel for reporting purposes.…”

Section: Literature Reviewmentioning

confidence: 99%

Spectrum Sensing Based on Hybrid Spectrum Handoff in Cognitive Radio Networks

Vaduganathan,

Neware,

Falkowski-Gilski

et al. 2023

Entropy

View full text Add to dashboard Cite

The rapid advancement of wireless communication combined with insufficient spectrum exploitation opens the door for the expansion of novel wireless services. Cognitive radio network (CRN) technology makes it possible to periodically access the open spectrum bands, which in turn improves the effectiveness of CRNs. Spectrum sensing (SS), which allows unauthorized users to locate open spectrum bands, plays a fundamental part in CRNs. A precise approximation of the power spectrum is essential to accomplish this. On the assumption that each SU’s parameter vector contains some globally and partially shared parameters, spectrum sensing is viewed as a parameter estimation issue. Distributed and cooperative spectrum sensing (CSS) is a key component of this concept. This work introduces a new component-specific cooperative spectrum sensing model (CSCSSM) in CRNs considering the amplitude and phase components of the input signal including Component Specific Adaptive Estimation (CSAE) for mean squared deviation (MSD) formulation. The proposed concept ensures minimum information loss compared to the traditional methods that consider error calculation among the direct signal vectors. The experimental results and performance analysis prove the robustness and efficiency of the proposed work over the traditional methods.

show abstract

“…The region of uncertainty is addressed with the help of a Fusion center that makes a cooperative decision on the presence of an available band. Some of the latest research as reported in [ 41 – 43 ], the extensive use of cooperative sensing clubbed with deep learning techniques promises to enhance the system performance and robustness. Furthermore, the sensing technique with the use of double threshold can be made more robust with optimization techniques as illustrated in [ 44 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Double Threshold-Based Spectrum Sensing Technique at Low SNR Under Noise Uncertainty for Cognitive Radio Systems

Mahendru

2022

Wireless Pers Commun

View full text Add to dashboard Cite

Cognitive Radio is a novel concept that has invoked a paradigm shift in wireless communication and promises to solve the problem of spectrum underutilization. Spectrum sensing plays a pivotal role in a cognitive radio system by detecting the vacant spectrum for establishing a communication link. For any spectrum sensing method, detection probability and error probability portray a significant part in quantifying the detection performance. At low SNR, it becomes cumbersome to differentiate noise and signal due to which sensing method loses robustness and reliability. In this paper, mathematical modeling and critical measurement of detection probabilities has been done for energy detection-based spectrum sensing at low SNR in uncertain noisy environment. A mathematical model has been proposed to compute double thresholds for reliable sensing when the observed energy is less than the uncertainty in the noise power. A novel parameter “Threshold Wall” has been formulated for optimum threshold selection to overcome sensing failure. Comparative simulation and analytical result measurements have been presented that reveals improved sensing performance.Please check inserted city is correct for affiliation 1.Noida, it is correct

show abstract

Enhancing the Spectrum Sensing Performance of Cluster-Based Cooperative Cognitive Radio Networks via Sequential Multiple Reporting Channels

Cited by 18 publications

References 56 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

Spectrum Sensing Based on Hybrid Spectrum Handoff in Cognitive Radio Networks

A Novel Double Threshold-Based Spectrum Sensing Technique at Low SNR Under Noise Uncertainty for Cognitive Radio Systems

Contact Info

Product

Resources

About