Combined Fusion Rules in Cognitive Radio Networks Using Different Threshold Strategies

Awin, Faroq; Salout, Noor; Abdel-Raheem, Esam

doi:10.3390/app9235080

Cited by 15 publications

(6 citation statements)

References 29 publications

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“…As shown in Figure 4, the simulation was performed under conditions the SNR of the PU's signal at the CR-IoT users varies from −10 dB to 0 dB under fading channels. In a Rayleigh fading channel [41][42][43], the signal amplitude follows a Rayleigh distribution, and the instantaneous SNR of the PU's signal at the unlicensed CR-IoT then follows an exponential distribution with a PDF that is given as follows:…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

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Enhanced Sensing and Sum-Rate Analysis in a Cognitive Radio-Based Internet of Things

Miah

Ahmed

Islam

et al. 2020

Sensors

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Spectrum sensing plays a vital role in cognitive radio networks (CRNs) for identifying the spectrum hole. However, an individual cognitive radio user in a CRN does not obtain sufficient sensing performance and sum rate of the primary and secondary links to support the future Internet of Things (IoT) using conventional detection techniques such as the energy detection (ED) technique in a noise-uncertain environment. In an environment comprising noise uncertainty, the performance of conventional energy detection techniques is significantly degraded owing to the noise fluctuation caused by the noise temperature, interference, and filtering. To mitigate this problem, we present a cooperative spectrum sensing technique that comprises the use of the Kullback–Leibler divergence (KLD) in cognitive radio-based IoT (CR-IoT). In the proposed method, each unlicensed IoT device that is capable of spectrum sensing, which is called a CR-IoT user, makes a local decision using the KLD technique. The spectrum sensing performed with the KLD requires a smaller number of samples than other conventional approaches, e.g., energy detection, for reliable sensing even in a noise uncertain environment. After the local decision is made, each CR-IoT user sends its own local decision result to the corresponding fusion center, which makes a global decision using the soft fusion rule. The results obtained through simulations show that the proposed KLD scheme achieves a better sensing performance, i.e., higher detection and lower false-alarm probabilities, enhances the sum rate, and reduces the total time as compared to the conventional ED scheme under various fading channels.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

“…where γ is the average SNR value. In a shadowing effect [41][42][43], the signal amplitude follows a log-normal distribution with a PDF that is given as follows:…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Enhanced Sensing and Sum-Rate Analysis in a Cognitive Radio-Based Internet of Things

Miah

Ahmed

Islam

et al. 2020

Sensors

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“…X(t) represents the sample of the received signal by UU [22], S(t) is the sample of the transmitted signal by LUs [23], and 𝑛(𝑡) is additive white gaussian noise (AWGN), N is the samples number [24]. Where Hi = (0,1) denotes the binary possibilities of a LU's presence or absence [25], The probability of detection (𝑃 𝑑 ) can be given:…”

Section: Methods 21 System Modelmentioning

confidence: 99%

Covariance absolute values spectrum sensing method based on two adaptive thresholds

Hashim

Ziboon

Abdulsatar

2023

IJEECS

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Cognitive radio is a modern wireless communication methodology that deals with the issue of spectrum untapped. Cognitive users can continually perceive the licensed spectrum to hunt for unoccupied spots. The essential technology in cognitive radio (CR) for primary user detection is spectrum sensing. Due to signal to noise ratio (SNR), noise uncertainty in spectrum sensing would make the detection unreliable. In this paper, the two adaptive thresholds based on_covariance absolute values (TATCAV) are proposed to increase detection performance in the presence of noise uncertainty. According to the computer simulations using MATLAB 2021b, the value of the probability of detection is Pd=98.1% Compared with the results of two thresholds based on_covariance absolute values (TTCAV) Pd= 95.3% at SNR=-18, noise uncertainty Nu=1.761 dB, and using quadrature amplitude modulation (QAM). And the error rate for the proposed approach is Pe=12.1% under the same circumstances. The proposed approach results, according to the simulations, are considerably better than the results of the fixed two-threshold approach.

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“…The cluster head reports sensing observations to the 𝐹𝐶 on behalf of its cluster members. [18] considers a CRN with multiple clusters and proposes three different threshold based strategies to improve detection performance as well as to reduce reporting overhead between the cluster heads and the 𝐹𝐶. In [19], concept of parallel reporting based on frequency division is adopted that reduces reporting time significantly.…”

Section: Related Workmentioning

confidence: 99%

A Coalition Formation Game for Cooperative Spectrum Sensing in Cognitive Radio Network under the Constraint of Overhead

Borgohain

Borkotokey

Deka

2021

Int. j. commun. netw. inf. secur.

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Cooperative spectrum sensing improves the sensing performance of secondary users by exploiting spatial diversity in cognitive radio networks. However, the cooperation of secondary users introduces some overhead also that may degrade the overall performance of cooperative spectrum sensing. The trade-off between cooperation gain and overhead plays a vital role in modeling cooperative spectrum sensing. This paper considers overhead in terms of reporting energy and reporting time. We propose a cooperative spectrum sensing based coalitional game model where the utility of the game is formulated as a function of throughput gain and overhead. To achieve a rational average throughput of secondary users, the overhead incurred is to be optimized. This work emphasizes on optimization of the overhead incurred. In cooperative spectrum sensing, the large number of cooperating users improve the detection performance, on the contrary, it increases overhead too. So, to limit the maximum coalition size we propose a formulation under the constraint of the probability of false alarm. An efficient fusion center selection scheme and an algorithm to select eligible secondary users for reporting are proposed to reduce the reporting overhead. We also outline a distributed cooperative spectrum sensing algorithm using the properties of the coalition formation game and prove that the utility of the proposed game has non-transferable properties. The simulation results show that the proposed schemes reduce the overhead of reporting without compromising the overall detection performance of cooperative spectrum sensing.

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Combined Fusion Rules in Cognitive Radio Networks Using Different Threshold Strategies

Cited by 15 publications

References 29 publications

Enhanced Sensing and Sum-Rate Analysis in a Cognitive Radio-Based Internet of Things

Enhanced Sensing and Sum-Rate Analysis in a Cognitive Radio-Based Internet of Things

Covariance absolute values spectrum sensing method based on two adaptive thresholds

A Coalition Formation Game for Cooperative Spectrum Sensing in Cognitive Radio Network under the Constraint of Overhead

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