Cooperative Spectrum Sensing Using Eigenvalue-Based Double-Threshold Detection Scheme for Cognitive Radio Networks

Charan, Chhagan; Pandey, Rajoo

doi:10.1007/978-981-13-1822-1_18

Cited by 7 publications

(5 citation statements)

References 9 publications

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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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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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“…The enormous increase in the number of mobile devices, combined with the stationary handling of frequency bands, has resulted in a lack of accessible available bandwidth [1]. According to the findings of a study conducted by the Federal Communication Commission (FCC), the percentage of licensing spectrum that is being continuously utilized by the licensed Primary User (PU) is lower than thirty percent [2]. According to FCC reports, there are bandwidth vacancies, particularly in the TV band, sometimes known as TV white s pace (TVWS) [3].…”

Section: Introductionmentioning

confidence: 99%

SDR-based Intelligent Cooperative Spectrum Sensing for Cognitive Radio Systems

Radhi

Akkar

Abdullah

2023

ETJ

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 The real-time sensing performance of the new intelligent cooperative spectrum sensing based on the denoised mixed feature method paired with K-M edoids is verified.  Two RTL-SDR hardware receivers, a host laptop computer, and a USRP-SDR hardware transmitter are used.  The theoretical and practical sensing performance validity of the developed scheme is confirmed based on the SDR platform.In this paper, the software and hardware of a software-defined radio (SDR) platform are used to implement and verify the blind real-time sensing act of intelligent collaborative spectrum sensing based on a new theoretical formula for constructing denoised mixed features named M SKU3 and paired with an unsupervised machine learning K-M edoids algorithm. Two low-cost RTL-SDR dongle hardware receivers are used as two cooperative unlicensed secondary users to capture the radio frequency of a licensed primary user channel. A host personal computer is used as a fusion center to run GNU-Radio software signal processing blocks to implement the developed method, and a single Universal Software Radio Peripheral (USRP) N210 hardware transmitter based on FPGA is used to take up unoccupied desired radio frequency bandwidth. Two scenarios of signal-to-noise ratio levels have been adopted to verify and test the sensing performance of the developed system. The first one occurs when unlicensed secondary users have equal signal-to-noise ratio values. The second occurs when unlicensed secondary users have different signal-to-noise ratio values since each secondary user has their location. The experimental results of detecting action in terms of the probability of detection for the proposed method show that the theoretical and practical results are very close to each other.

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“…In SS, AU scans through the assigned spectrum to identify unused spectrum required by the CU. This is achieved either by using only one CU known as Non-Cooperative Sensing (NCS) or group of CUs known as Cooperative Sensing (CS) [Abbas et al, 2021;Noor, 2017;Gevira, 2016;Chhagan and Rajoo, 2019] receiver uncertainty which occurs when CU is outside the transmission range of the AU as a result of effect of multipath propagation. The CS, on the other hand, solves the challenges of NCS and achieves a reliable detection rate.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of blind detectors in a cluster-based cooperative Spectrum Hole detection

Ojo¹,

Adeyemo²

2023

Nig. J. Technol. Dev.

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Prevention of authorized users from interference determine the accurate detection of Spectrum Hole (SH) is of great importance in a Spectrum Shearing Network (SSN). However, multipath fading and shadowing affect the accurate detection of SH resulting in interference. Cluster-Based Cooperative Spectrum Hole Detection (CBCSHD) used to address this problem depends on detector and number of clusters. Hence, comparative analysis of blind detectors in CBCSHD is carried out to evaluate its performance with various blind detectors and number of clusters. The CBCSHD is carried out using six Cognitive Users (CUs) that jointly carry out detection of SH and each of the CUs performs local sensing using Eigenvalue Detector (EVD), Energy Detector (ED) and Cyclostationary Detector (CD). The CUs form clusters to reduce reporting overhead between CUs. The local sensing results from individual user are combined at the Cluster Head (CH) using majority fusion rule. The performance of each of the detectors in CBCSHD is evaluated using Probability of Detection (PD) and Sensing Time (ST). PD values of 0.7661, 0.7160 and 0.6229 are obtained at SNR of 4 dB for ED, CD and EVD, respectively, while ST values of 3.0707, 3.7163 and 4.0907 s are obtained for ED, CD and EVD, respectively. The results obtained show that ED has the highest detection rate, followed by CD, while EVD shows the worst detection rate.

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Cooperative Spectrum Sensing Using Eigenvalue-Based Double-Threshold Detection Scheme for Cognitive Radio Networks

Cited by 7 publications

References 9 publications

Covariance absolute values spectrum sensing method based on two adaptive thresholds

Covariance absolute values spectrum sensing method based on two adaptive thresholds

SDR-based Intelligent Cooperative Spectrum Sensing for Cognitive Radio Systems

Comparative analysis of blind detectors in a cluster-based cooperative Spectrum Hole detection

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