Effect of spectrum sensing and transmission duration on spectrum hole utilisation in cognitive radio networks

Ali, Mohsin; Nam, Haewoon

doi:10.1049/iet-com.2017.0576

Cited by 8 publications

(5 citation statements)

References 19 publications

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“…A higher value of N guarantees higher (P d ) and lower (P f ). So, (P d ) and (P f ) can be expressed as follows [23]:…”

Section: Received Signal Energy Detectionmentioning

confidence: 99%

“…A high value of P d and lower value of P f are always required to avoid interference by SUs to PUs. There exists a trade-off between the optimal sensing time and spectrum hole utilization in cognitive radio networks [23]. A solution of an optimization problem maximizes the spectrum utilization efficiency of secondary users by considering the different possible communication scenarios of SUs in CRN [24].…”

Section: Introductionmentioning

confidence: 99%

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Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach

Raza,

Ali,

Ehsan

et al. 2023

Sensors

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The rapid technological advancements in the current modern world bring the attention of researchers to fast and real-time healthcare and monitoring systems. Smart healthcare is one of the best choices for this purpose, in which different on-body and off-body sensors and devices monitor and share patient data with healthcare personnel and hospitals for quick and real-time decisions about patients’ health. Cognitive radio (CR) can be very useful for effective and smart healthcare systems to send and receive patient’s health data by exploiting the primary user’s (PU) spectrum. In this paper, tree-based algorithms (TBAs) of machine learning (ML) are investigated to evaluate spectrum sensing in CR-based smart healthcare systems. The required data sets for TBAs are created based on the probability of detection (Pd) and probability of false alarm (Pf). These data sets are used to train and test the system by using fine tree, coarse tree, ensemble boosted tree, medium tree, ensemble bagged tree, ensemble RUSBoosted tree, and optimizable tree. Training and testing accuracies of all TBAs are calculated for both simulated and theoretical data sets. The comparison of training and testing accuracies of all classifiers is presented for the different numbers of received signal samples. Results depict that optimizable tree gives the best accuracy results to evaluate the spectrum sensing with minimum classification error (MCE).

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“…A higher value of N guarantees higher (P d ) and lower (P f ). So, (P d ) and (P f ) can be expressed as follows [23]:…”

Section: Received Signal Energy Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach

Raza,

Ali,

Ehsan

et al. 2023

Sensors

Self Cite

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“…Ali and Nam 31 have suggested the swap between the probability of spectrum hole utilization (PSHU) and the duration of detection time of SU in CRN. Systematic PSHU provided optimal sensing duration and time frame length for PU unused time.…”

Section: Literature Surveymentioning

confidence: 99%

A chaotic whale optimization algorithm (CWOA) based multiobjective conjoint spectrum utilization in a cognitive radio (CR) network

Swathi,

Prakash,

Krishna

2023

Int J Communication

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SummaryIn this manuscript, a novel energy detection‐based chaotic whale optimization algorithm (ED‐CWOA) is proposed to maximize the spectrum utilization, duration, and threshold in cognitive radio network (CRN). Here, the spectrum utilization is designed taking into account sensing duration and threshold in multi‐objective function is formulated by considering its primary user (PU) protection restrictions and secondary user (SU) throughput. To get the global optimal solution, the CWOA and the multi‐objective optimization problem are finally carried out on a single objective. The execution is performed on NS2 simulator. The simulation outcome demonstrates that the proposed ED‐CWOA system has lower delay 33.878%, 26.206%; higher delivery ratio 35.536%, 22.65%; lower Fairness Index 46.66%, 58.332%; lower energy consumption 25.462%, 35.524%; and higher throughput 96.82%, 49.28% for different nodes analyzed with the existing methods, like energy detection‐based bivariate Levy‐stable bat algorithm (ED‐BLSBA) and genetic algorithm‐based energy detection (ED‐GA), respectively. The simulation outcome indicates that the proposed system may be able to get the optimal global solutions efficiently and accurately.

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“…Among the presented schemes, energy detection scheme is the most commonly used approach, because of its simplicity to implement and relatively low computational complexity. In addition, the energy detection schemes do not need a priori information about the PU's transmit parameters [9–11]. However, the spectrum‐sensing process is usually imperfect and is with error [11–13].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the energy detection schemes do not need a priori information about the PU's transmit parameters [9–11]. However, the spectrum‐sensing process is usually imperfect and is with error [11–13]. Therefore, to ensure a relatively accurate spectrum sensing, these sensing errors should be taken into consideration.…”

Section: Introductionmentioning

confidence: 99%

Optimal power control and spectrum sensing for throughput maximisation in cognitive radio systems under PUEA

et al. 2019

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Effect of spectrum sensing and transmission duration on spectrum hole utilisation in cognitive radio networks

Cited by 8 publications

References 19 publications

Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach

Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach

A chaotic whale optimization algorithm (CWOA) based multiobjective conjoint spectrum utilization in a cognitive radio (CR) network

Optimal power control and spectrum sensing for throughput maximisation in cognitive radio systems under PUEA

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