Robust spectrum sensing based on statistical tests

Arshad, Kamran; Moessner, Klaus

doi:10.1049/iet-com.2012.0499

Cited by 18 publications

(11 citation statements)

References 13 publications

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“…Considering the small number of samples and the special sensing scenario about detecting a wideband signal in a zero mean Gaussian noise, it is shown that the optimal test in signal detection is Student's t test [31]. In order to construct the test statistic in accordance with Student's t distribution, we denote, respectively, X k and S 2 k as the mean and variance of the samples in the kth subband,…”

Section: Statistical Model Of the Received Small Samplesmentioning

confidence: 99%

“…In order to improve the reliability, Dempster-Shafer (D-S) theory of evidence [22][23][24][25][26][27][28][29][30] is used to make a final decision. As in [31], in the proposed method, Student's t distribution of a reduced number of samples is used. The main contribution stands in the proposition of two new BPA functions to evaluate the credibility of the collected small samples from a wideband signal and the combination of BPA functions in order to make a more reliable decision.…”

Section: Introductionmentioning

confidence: 99%

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Wideband signal detection for cognitive radio applications with limited resources

Men

Chargé

Wang

et al. 2019

EURASIP J. Adv. Signal Process.

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Wideband signals are expected to be used to achieve the required quality of service (QoS) in the next generation of wireless communications, civil and military radar, and many wireless sensor network (WSN) scenarios. Wideband signal detection has been identified as one of the most challenging problems in the proliferation of the cognitive radio technology. Moreover in many applications, spectrum sensing in cognitive radio (CR) is expected to be performed with limited resources in terms of time, computation, and complexity. This paper is dedicated to the detection of a wideband signal with small sample size. Aiming at using small sample size, a statistical model of samples is given based on Student's t distribution. However, the limited number of channel observations brings a reduction of confidence in the decision. A set of new basic probability assignments associated with the hypothesis of the occupied or vacant channel are then proposed to perform the Dempster-Shafer (D-S) decision process. Simulation results show that the proposed method has much higher sensitivity to sense an occupied channel than the traditional energy detection method (ED) and the decision fusion method when small sample size is used.

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Section: Statistical Model Of the Received Small Samplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wideband signal detection for cognitive radio applications with limited resources

Men

Chargé

Wang

et al. 2019

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…It is shown that this combiner does not demand the CSI, and it provides superior performance compared to MRC. The Goodness of fit test based sensing (GoFT) using Anderson Darling (AD) and Jarque Bera (JB) tests check for the distribution of test statistic under null hypothesis and is independent of the noise distribution [9][10][11]. These statistical methods were primarily meant to measure the strength of evidence for drawing an accurate decision in hypothesis testing based on a sample.…”

Section: Introductionmentioning

confidence: 99%

A statistical approach to spectrum sensing using bayes factor and p-Values

Reddy¹,

Ravinder²

2019

IJECE

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<p>The sensing methods with multiple receive antennas in the Cognitive Radio (CR) device, provide a promising solution for reducing the error rates in the detection of the Primary User (PU) signal. The received Signal to Noise Ratio at the CR receiver is enhanced using the diversity combiners. This paper proposes a statistical approach based on minimum Bayes factors and p-Values as diversity combiners in the spectrum sensing scenario. The effect of these statistical measures in sensing the spectrum in a CR environment is investigated. Through extensive Monte Carlo simulations it is shown that this novel statistical approach based on Bayes factors provides a promising solution to combine the test statistics from multiple receiver antennas and can be used as an alternative to the conventional hypothesis testing methods for spectrum sensing. The Bayesian results provide more accurate results when measuring the strength of the evidence against the hypothesis.</p>

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“…In a two-sample K-S test, the basic procedure involves computing the empirical cumulative distribution function of given datasets, and comparing with each other. It has been applied to fast spectrum sensing [22,23] and feature selection in emotional speech recognition [24].…”

Section: Introductionmentioning

confidence: 99%

Dissolved gas analysis method based on novel feature prioritisation and support vector machine

Wei

Tang

2014

IET Electric Power Applications

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Dissolved gas analysis (DGA) has been widely used for the detection of incipient faults in oil-filled transformers. This research presents a novel approach to DGA feature prioritisation and classification, which considers not only the relations between a fault type and specific gas ratios but also their statistical characteristics based on data derived from onsite inspections. Firstly, new gas features are acquired based on the analysis of current international gas interpretation standards. Combined with conventional gas ratios, all features are then prioritised by using the Kolmogorov-Smirnov test. The rankings are obtained by using their values of maximum statistic distance. The first three features in ranking are employed as input vectors to a multilayer support vector machine, whose tuning parameters are acquired by particle swarm optimisation. In the experiment, a bootstrap technique is implemented to approximately equalise sample numbers of different fault cases. A common 10-fold cross-validation technique is employed for performance assessment. Typical artificial intelligence classifiers with gas features extracted from genetic programming are evaluated for comparison purposes.

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Robust spectrum sensing based on statistical tests

Cited by 18 publications

References 13 publications

Wideband signal detection for cognitive radio applications with limited resources

Wideband signal detection for cognitive radio applications with limited resources

A statistical approach to spectrum sensing using bayes factor and p-Values

Dissolved gas analysis method based on novel feature prioritisation and support vector machine

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