Energy Detection for Spectrum Sensing in Cognitive Radio

Atapattu, Saman; Tellambura, Chintha; Jiang, Hai

doi:10.1007/978-1-4939-0494-5

Cited by 169 publications

(109 citation statements)

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“…As a consequence, T (x) follows a central Chi-square distribution under hypothesis H 0 and non-central Chi-square distribution under hypothesis H 1 [13]. Using the central limit theorem, for large N we have the following [13,14],…”

Section: Distribution Of the Test Statistic And Detection Performancementioning

confidence: 98%

Compressive sensing based energy detector

Lagunas

Sharma

Chatzinotas

et al. 2016

2016 24th European Signal Processing Conference (EUSIPCO)

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While most of research in Compressive Sensing (CS) has been focused on reconstruction of the sparse signal from very fewer samples than the ones required by the Shannon-Nyquist sampling theorem, recently there has been a growing interest in performing signal processing directly in the measurement domain. This new area of research is known in the literature as Compressive Signal Processing (CSP). In this paper, we consider the detection problem using a reduced set of measurements focusing on the Energy Detector (ED), which is the optimal Neyman-Pearson (NP) detector for random signals in Gaussian noise. In particular, we provide simple closed form expressions for evaluating the detection performance of ED when considering compressive measurements. The resulting equations reflect the loss due to CS and allow to determine the minimum number of samples to achieve certain detection performance.

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Section: Distribution Of the Test Statistic And Detection Performancementioning

confidence: 98%

Compressive sensing based energy detector

Lagunas

Sharma

Chatzinotas

et al. 2016

2016 24th European Signal Processing Conference (EUSIPCO)

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“…It is pointed out in [2,12] that increase of Δλ increases the 'No decision' region thereby resulting in poor sensing performance compared to the above two scenarios.…”

Section: Scenariomentioning

confidence: 99%

Improved Energy Detector for Spectrum Sensing Using Neuro-Fuzzy Double Threshold Technique

Dey¹,

Hossain²,

Balas³

et al. 2017

STUD INFORM CONTROL

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This paper reports a modified and computationally efficient energy detection method for spectrum sensing in cognitive radio (CR) system utilising hybrid soft computing paradigms. The proposed soft computing method uses artificial neural network and fuzzy logic for learning and decision making as a solution to the problems when CR system is subjected to threshold uncertainty and uncertainties in noise variance. The proposed soft computing method should be referred thereafter as Neuro-Fuzzy double threshold (NFDT) technique. The developed technique utilizes the concept of double sensing credibility while formulating the fuzzy decision rule and artificial neural network to learn the threshold function. Through simulation results it is validated that proposed method leads to overall improved probability of detection compared to conventional and existing methods.

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“…Taking square ofX kl in (1) provides the usual energy detector and is shown to be optimal for Gaussian noise in the absence of S k statistics. However, it has been shown [6] that for non Gaussian noise, instead of 2, some other power p of |X kl | may perform better. In the following we will keep p = 2 but allow the possibility of other powers when EMI is significant (see below).…”

Section: System Model and Distributed Algorithmmentioning

confidence: 99%

“…Our aim of using ψ for heavy-tailed case is to create light-tailed samples (7). In our simulations below for energy samples, we will take K large for ψ 1 (≈ 200) but small (≤ 5) for ψ in (4) and (6).…”

Section: Mitigating Effects Of Outliers Heavy Tails and Fadingmentioning

confidence: 99%

“…Furthermore, the transmit power, channel gains, coding and modulations of the primary are unknown and hence standard algorithms such as matched filter or cyclostationarity detector ( [3]) may not be available. Energy detection (or generalised energy detection [6]) is found to be the technique applicable in such scenarios. Lack of complete knowledge about the signal and the channel fading (shadowing) calls for nonparametric (or semiparametric) detection algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Distributed nonparametric sequential spectrum sensing under electromagnetic interference

Sahasranand

Sharma

2015

2015 IEEE International Conference on Communications (ICC)

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Abstract-A nonparametric distributed sequential algorithm for quick detection of spectral holes in a Cognitive Radio set up is proposed. Two or more local nodes make decisions and inform the fusion centre (FC) over a reporting Multiple Access Channel (MAC), which then makes the final decision. The local nodes use energy detection and the FC uses mean detection in the presence of fading, heavy-tailed electromagnetic interference (EMI) and outliers. The statistics of the primary signal, channel gain or the EMI is not known. Different nonparametric sequential algorithms are compared to choose appropriate algorithms to be used at the local nodes and the FC. Modification of a recently developed random walk test is selected for the local nodes for energy detection as well as at the fusion centre for mean detection. It is shown via simulations and analysis that the nonparametric distributed algorithm developed performs well in the presence of fading, EMI and is robust to outliers. The algorithm is iterative in nature making the computation and storage requirements minimal.

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Energy Detection for Spectrum Sensing in Cognitive Radio

Cited by 169 publications

References 0 publications

Compressive sensing based energy detector

Compressive sensing based energy detector

Improved Energy Detector for Spectrum Sensing Using Neuro-Fuzzy Double Threshold Technique

Distributed nonparametric sequential spectrum sensing under electromagnetic interference

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