Performance Analysis of Energy Detection Based Spectrum Sensing with Unknown Primary Signal Arrival Time

Wu, Jwo-Yuh; Wang, Chih-Hsiang; Wang, Tsang-Yi

doi:10.1109/tcomm.2011.050211.100146

Cited by 35 publications

(25 citation statements)

References 10 publications

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“…In this paper, we generalize the result in [9] by further taking into account the effect of random departure of the primary user's signal. Specifically, we consider the following model for the binary hypothesis test:…”

Section: [N] X[n] and V[n]mentioning

confidence: 89%

“…It is worthy of nothing that, in [6][7][8], the characterization of the detection/false-alarm probabilities resort to certain approximation techniques, e.g., the central limit theorem or the Chi-square approximation. In [9], the authors focused on the case with random arrival, and derived the exact detection probability of ED; a Bayesianbased ED is also proposed to improve the robustness of the detection performance against random signal arrival.…”

Section: [N] X[n] and V[n]mentioning

confidence: 99%

“…Remark : When n 1 is equal to N − 1, the signal model (2) is reduced to the random arrival case considered in [9]. The conditional probability P D (n 0 , n 1 ) in (13) is simplified to…”

Section: A Conditional Detection Probabilitymentioning

confidence: 99%

“…Following [9], in this section we propose a Bayesian-based ED by exploiting the statistical knowledge of n 0 and n 1 . The conditional pmf of the data samples under hypotheses H 0 and H 1 are, respectively,…”

Section: A Bayesian Decision Rulementioning

confidence: 99%

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Energy detection based spectrum sensing with random arrival and departure of primary user's signal

Huang

Wang

et al. 2013

2013 IEEE Globecom Workshops (GC Wkshps)

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Abstract-Energy detection (ED) is a popular spectrum sensing technique for cognitive radios. The study of ED which takes into account the dynamic of traffic patterns of primary users, in the form of random signal arrival and departure, is of both theoretical and practical importance. Some of the existing works, however, resort to certain approximation techniques to characterize the detection performance. In this paper, given a pair of arrival and departure time instants, we first derive an exact expression for the conditional detection probability. The exact mean detection probability is then obtained via an average operation over the random arrival and departure times. To improve the robustness of the detection performance against random signal arrival and departure, we further propose a Bayesian-based ED scheme. We present simulation results to validate our analytic study, and show the performance gain of our proposed Bayesian approach.

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Section: [N] X[n] and V[n]mentioning

confidence: 89%

Section: [N] X[n] and V[n]mentioning

confidence: 99%

“…Remark : When n 1 is equal to N − 1, the signal model (2) is reduced to the random arrival case considered in [9]. The conditional probability P D (n 0 , n 1 ) in (13) is simplified to…”

Section: A Conditional Detection Probabilitymentioning

confidence: 99%

Section: A Bayesian Decision Rulementioning

confidence: 99%

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Energy detection based spectrum sensing with random arrival and departure of primary user's signal

Huang

Wang

et al. 2013

2013 IEEE Globecom Workshops (GC Wkshps)

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“…It is very difficult to obtain the distribution of the test statistic in (15), since it is a combination of the four random variables. To obtain a closed-form expression, we ignore the noise and make the following approximation,…”

Section: B the Distribution Of The Test Statisticmentioning

confidence: 99%

Enhancing Small Cell Transmission Opportunity Through Passive Receiver Detection in Two-Tier Heterogeneous Networks

Zhao

Zhou

2015

IEEE Trans. Signal Process.

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Retracted: A hybrid‐supervised learning model for compressive spectrum sensing in fifth generation(5G) cognitive Radio enabled internet of things (IoT) networks

Jothiraj¹,

Sridevi²,

Rangaraj

2021

Int J Communication

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Summary 5G Internet of Things (IoT) networks are characterized by wideband radio frequency spectrum utility and are therefore of primary importance for efficient means of sensing the wideband spectrum characterized by high bandwidth. A cognitive radio network (CRN) intelligently does the sensing of authorized users ideal spectrum and allocates the same to the demanded unauthorized user. Conventional energy detection and k‐means schemes associated with CRN perform well for narrowband applications, whereas they are not quite suitable for wideband applications. Hence, a compressive collaborative sensing scheme together with deep neural network learning model (CCS‐DLNN) has been proposed to sense the information from the compressed and reconstructed information signal. Based on the extracted features, decision on presence or absence of primary user (PU) in the received signal has been observed. This paper proposes a deep learning neural network model for learning the dynamic change in the input spectra. Accordingly, this paper also updates the weights associated with the neurons to converge upon the target objective. The performance of the proposed sensing scheme has been evaluated pertaining to probability of detection, sensing error, and accuracy of detection of idle channels. The proposed work will be very useful for the upcoming generation departing to be implemented with 5G IoT networks.

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Performance Analysis of Energy Detection Based Spectrum Sensing with Unknown Primary Signal Arrival Time

Cited by 35 publications

References 10 publications

Energy detection based spectrum sensing with random arrival and departure of primary user's signal

Energy detection based spectrum sensing with random arrival and departure of primary user's signal

Enhancing Small Cell Transmission Opportunity Through Passive Receiver Detection in Two-Tier Heterogeneous Networks

Retracted: A hybrid‐supervised learning model for compressive spectrum sensing in fifth generation(5G) cognitive Radio enabled internet of things (IoT) networks

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