Spectrum Sensing Using Cognitive Radio Technology

Meena, M.; Bhagari, F.; Rajendran, V.

doi:10.1109/iccsp.2017.8286672

Cited by 10 publications

(4 citation statements)

References 4 publications

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“…Time domain operation would need of primary signal front-end filtering. In scenario of multiple-filters are essential; if the more than likewise one primary-user signal with the distinct spectrum bands is expected [8]. Here in the frequency-domain operation, the conventional broad-band front-end filtering process is enough and adequate and samples of received signal are first converted and transformed to the frequency-domain samples before and ahead of squaring device operation.…”

Section: Hs 1 Fig 1: System Model Of Energy Spotting Methodsmentioning

confidence: 99%

Cognitive Radio Network based on Energy Spotting Method with Enhanced Sensing Accuracy

Kumar*¹,

Rajesha²,

Deshpande³

2019

IJITEE

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This paper represents the unique system model for cognitive radio based on the energy spotting method to enhance the performance of the accuracy by managing the queue regarding energy-samples and also estimating their average in order to characterize the decision-threshold. Consequently, these typical values summed and estimated over the sum of the samples are repeatedly correlated and analyzed with the recent energy values to determine whether the frequency band is vacant or occupied most accurately. The energy spotting technique’s performance is analyzed and estimated analytically for distinct decision-thresholds. Conventionally Such evaluations interprets that; the advances made to energy spotting algorithm which have enhanced the sensing accuracy in spectrum under the differing signal-to-noise ratio values. Consequently, we shown the utilities and advantages of proposed model that increases the cognitive radio ability. The performance has measured by utilizing the AWGN (Additive White Gaussian Noise) channel and receiver operating-characteristics curves varying under various SNR values alike as: -20 dB, -15 dB, -5 dB, 0 dB, 5db and 10db. With small-tradeoffs among the detection and false-alarm probabilities, the model increases and enhances the ability of spectrum sensing mechanism greatly in the lower SNR situations while tested with number of samples. By that, improving the conventional performance by increasing the sensing accuracy of cognitive radio networks under the low SNR have been the promising achievement of this research work.

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Section: Hs 1 Fig 1: System Model Of Energy Spotting Methodsmentioning

confidence: 99%

Cognitive Radio Network based on Energy Spotting Method with Enhanced Sensing Accuracy

Kumar*¹,

Rajesha²,

Deshpande³

2019

IJITEE

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“…Hs 0 (1) q(n) = p(n) + r(n) Hs 1 (2) Whereas Hs 0 is the hypothesis which PU is not using its frequency band and Hs 1 is the hypothesis which cognitive PU using the frequency band for its act of transmission, q(n) is received-signal at cognitive secondary-user; p(n) is the AWGN (Additive White Gaussian Noise) sample having the variance . The hypotheses s 0 say about the primary user unavailability and the s 1 represents about PUs availability.…”

Section: Hs 1 Fig 1: System Model Of Energy Spotting Methodsmentioning

confidence: 99%

Declining of Sensing Errors in Cognitive Radio by Threshold Determination of Energy Spotting Method

2019

ijrte

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The conventional Cognitive Radio Network is anticipates the fundamental solution to problem of spectrum scarcity in the forthcoming wireless and cellular communication networks, furthermore CR networking senses vacant spectrum bands in opportunistic method, and though, increases the effectiveness of spectrum usage. The CRN (Cognitive radio Network) provides the wireless connectivity and integration through the medium of dynamic spectrum access approaches and heterogeneous wireless designs and architectures. The enabling techniques and utilities of the CRNs have received the dynamic research and the industry interest. In this specific paper, we proposed an solution to decrease the error rate by enhancing the performance of detection which occur in time of spectrum sensing mechanism.

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“…With the development of Mobile Communication Technology and Internet of Things (IoT), the traditional static spectrum allocation strategy becomes difficult to meet the increasing demand of wireless services [1][2][3]. On this condition, the Cognitive Radio (CR) technology emerges as a candidate to solve the problem of shortage of spectrum resources [4,5]. The unlicensed users (secondary users, (SUs)) in cognitive radio network (CRNs) are able to sense the spectrum of the licensed users (primary users, (PUs)) and use the unoccupied spectrum without influencing the Quality of Service (QoS).…”

Section: Introductionmentioning

confidence: 99%

Asynchronous cooperative spectrum sensing via sequential detection in fuzzy hypothesis testing under noise uncertainty in cognitive radio networks

Xie

2022

IET Communications

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Spectrum sensing is a critical part in Cognitive radio networks to detect the available frequency resources, and its timeliness and accuracy are extremely important. The conventional detectors usually fail to be robust in the presence of noise uncertainty, hence an asynchronous cooperative spectrum sensing approach based on sequential probability ratio detection in fuzzy hypothesis testing is proposed. In the approach, sequential detection in fuzzy hypothesis testing is done by each cognitive radio to form local decisions, then fusion centre sequentially accumulates the local decisions of cooperative cognitive radios to make a global decision. Simulation results show that the proposed approach can reduce the average number of samples and overcome noise uncertainty when compared with conventional or selective asynchronous cooperative sensing and non‐cooperative sensing.

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Spectrum Sensing Using Cognitive Radio Technology

Cited by 10 publications

References 4 publications

Cognitive Radio Network based on Energy Spotting Method with Enhanced Sensing Accuracy

Cognitive Radio Network based on Energy Spotting Method with Enhanced Sensing Accuracy

Declining of Sensing Errors in Cognitive Radio by Threshold Determination of Energy Spotting Method

Asynchronous cooperative spectrum sensing via sequential detection in fuzzy hypothesis testing under noise uncertainty in cognitive radio networks

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