Optimization for Prediction-Driven Cooperative Spectrum Sensing in Cognitive Radio Networks

Nie, Dawei; Yu, Wenjuan; Ni, Qiang; Pervaiz, Haris

doi:10.1109/iccworkshops53468.2022.9814552

Cited by 2 publications

(4 citation statements)

References 16 publications

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“…Nie et al [13] developed a new cluster-based cooperative detecting after-forecast system where a learning and sensing group are together measured for implementing cooperative forecast as well as sensing proficiently. This permits to skipping difficult physical detecting to decrease demands when spectrum accessibility can be just projected utilizing cooperative forecast.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Deep Learning Empowered Malicious User Detection for Spectrum Sensing in Cognitive Radio Networks

Almuqren,

Maray,

Alotaibi

et al. 2024

IEEE Access

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Malicious user recognition for spectrum sensing in Cognitive Radio Networks (CRNs) is a serious safety feature to safeguard effective and trustworthy process of these systems. Spectrum sensing permits CRNs to identify and employ accessible spectrum bands. As well as it is available to prospective interference and mischievous actions. To preserve network integrity, recognition of malicious consumers is vital. Deep learning (DL) based malicious consumer classification powers advanced neural network frameworks to recognize and flag possible threats inside a network. By examining numerous amounts of information, DL techniques can distinguish patterns as well as anomalies that are connected with malicious user performance plus system intrusions, scams or irregular action. This technique provides flexibility benefit that permits a network to learn and develop in evolving threats. It also offers an effectual revenue of improving network security in the difficult and active digital landscape. Therefore, this article develops an Optimal Deep Learning Empowered Malicious User Detection for Spectrum Sensing (ODL-MUDSS) in the CRN. The main intention of ODL-MUDSS model focused on automated identification and classification of MUs in CRN. To accomplish this, the ODL-MUDSS model primarily applies deep belief network (DBN) methodology for automated and accurate detection of MUs. In addition, recognition performance of DBN technique can be enhanced by use of sand cat swarm optimization (SCSO) algorithm and thereby improves the detection results. The performance validation of ODL-MUDSS technique is observed under different processes. The comprehensive outcomes stated enhanced performance of ODL-MUDSS model over other existing models with maximum accuracy of 97.75%, precision of 97.75%, recall of 97.75%, and F-score of 97.75%.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal Deep Learning Empowered Malicious User Detection for Spectrum Sensing in Cognitive Radio Networks

Almuqren,

Maray,

Alotaibi

et al. 2024

IEEE Access

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“…Recall (1) indicates that if at least σ prediction results are the same, the system will be in DM state and accept the CP result. Because the CP result can be either correct (with a probability of P acc ) or wrong, the probability of entering DM state can be calculated as the probability of at least σ predictions being right plus the probability of at least σ predictions being wrong.…”

Section: A Prediction Stagementioning

confidence: 99%

“…The scarcity of useful radio spectrum and energy consumption issue in cellular networks are getting severe This work was supported by the European Union's Horizon 2020 SANCUS project under the grant number GA952672. Part of this work was presented at the IEEE ICC 2022 DDINS workshop [1]. (Corresponding author: Qiang Ni.)…”

Section: Introductionmentioning

confidence: 99%

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Cluster Control and Energy Consumption Minimization for Cooperative Prediction Based Spectrum Sensing in Cognitive Radio Networks

Nie

et al. 2023

IEEE Trans. Commun.

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Spectrum sensing is a key technique for dynamically detecting available spectrum in cognitive radio networks (CRNs), which can introduce high resource demands such as energy consumption. In this paper, we propose a novel cluster-based cooperative sensing-afterprediction scheme where a learning cluster and a sensing cluster are jointly considered to perform cooperative prediction and sensing efficiently. This enables us to skip the complex physical sensing to reduce the demands when the spectrum availability can be simply predicted using cooperative prediction. Furthermore, the clustering is flexible, in order to meet different performance requirements. We then formulate two optimization problems to minimize the total number of users in the two clusters or to minimize the total energy consumption, to meet different performance requirements, while in both cases guaranteeing the system accuracy requirement and individual energy constraints. To solve the two challenging integer programming problems, the unconstrained problems are mathematically solved first by relaxing the integer variable and fixing the cluster size. Such analytical solutions serve as a foundation for solving the original optimization problems. Then, two lowcomplexity search algorithms are proposed to achieve the global optimum, as they can obtain the same performance with exhaustive search. Simulation results validate the accuracy of the derived analytical expressions and demonstrate that the total energy consumption and the number of users contributing to learning and sensing can be greatly reduced by applying our optimized clustered sensing-afterprediction scheme.

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Optimization for Prediction-Driven Cooperative Spectrum Sensing in Cognitive Radio Networks

Cited by 2 publications

References 16 publications

Optimal Deep Learning Empowered Malicious User Detection for Spectrum Sensing in Cognitive Radio Networks

Optimal Deep Learning Empowered Malicious User Detection for Spectrum Sensing in Cognitive Radio Networks

Cluster Control and Energy Consumption Minimization for Cooperative Prediction Based Spectrum Sensing in Cognitive Radio Networks

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