Spectrum sensing in cognitive radio using goodness of fit testing

Wang, Haiquan; Yang, En-hui; Zhao, Zhijin; Zhang, Wei

doi:10.1109/twc.2009.081586

Cited by 123 publications

(133 citation statements)

References 17 publications

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“…In [6], Anderson-Darling (AD) based goodness of fit test is proposed for spectrum sensing. In this paper, to avoid the estimation of noise variance, we apply the algorithm to test the normalized eigenvalues.…”

Section: Anderson-darling Based Sensing With Normalized Eigenvaluesmentioning

confidence: 99%

“…Therefore, spectrum sensing can be formulated as a goodness of fit testing problem. In this paper, the goodness of fit test based on Anderson-Darling is adopted [6]. The steps are given as follows:…”

Section: Anderson-darling Based Sensing With Normalized Eigenvaluesmentioning

confidence: 99%

“…To the best of the authors' knowledge, this is the first time to get those closed-form expressions. Using the random matrix results, we apply the goodness of fit test [6] for the ratio of an unordered eigenvalue to the trace of the sample covariance matrix. The performance of the spectrum sensing algorithm is evaluated for cooperative spectrum sensing [7].…”

Section: Introductionmentioning

confidence: 99%

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Distribution of the ratio of an unordered eigenvalue to the trace of a complex central Wishart matrix and its application to cooperative spectrum sensing

Wang

Wei

et al. 2017

IEICE ComEX

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In this paper, we derive the closed-form expressions of the probability distribution function (PDF) and cumulative distribution function (CDF) of the ratio of an unordered eigenvalue to the trace of a complex central Wishart matrix. Using the random matrix result, we study the eigenvalue-based spectrum sensing algorithm for cognitive ratio. We apply the goodness of fit test to the ratio of an unordered eigenvalue to the trace of the sample covariance matrix. Finally, we compare the performances between the proposed algorithm and the well-known eigenvalue-based spectrum sensing algorithms in cooperative systems.

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Section: Anderson-darling Based Sensing With Normalized Eigenvaluesmentioning

confidence: 99%

Section: Anderson-darling Based Sensing With Normalized Eigenvaluesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Distribution of the ratio of an unordered eigenvalue to the trace of a complex central Wishart matrix and its application to cooperative spectrum sensing

Wang

Wei

et al. 2017

IEICE ComEX

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“…Our proposed schemes do not require a-priori knowledge of the incumbent (or opportunistic) user signal and hence we classify them as non-parametric schemes. We compare the sensing performance of our methods with the well-known ED sensing and AD sensing proposed in [6]. For KS sensing we used the assumption that the noise probability distribution is known to the user, as in [6].…”

mentioning

confidence: 99%

“…However, it needs a short sequence of noise samples which can be measured from a reporting channel (where it is sure that a transmitted signal is not present). It should be noted that this assumption is essentially the same as used in the well known ED (and other sensing algorithms like AD sensing [6]). Our scheme, t-sensing, compares the received signal samples with the noise samples and makes a decision.…”

mentioning

confidence: 99%

Robust spectrum sensing based on statistical tests

Arshad¹,

Moessner

2013

IET Communications

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Spectrum sensing, in particular, detecting the presence of licensed or incumbent users in licensed spectrum, is one of the pivotal tasks in cognitive radio network. In this paper, we tackle the spectrum sensing problem by using statistical test theory and derive novel spectrum sensing approaches. We apply the classical Kolmogorov-Smirnov (KS) test under the assumption that the noise probability distribution is known. However, as in practice, the exact noise distribution is unknown, a sensing method for Gaussian noise with unknown noise power is proposed in this article and refer as t-sensing. The proposed sensing scheme is asymptotically robust and can be applied to non-Gaussian noise distributions. A closed form equation determining the miss detection probability for the t-sensing is derived. We compare the performance of our sensing algorithms with the Energy Detector (ED) and Anderson-Darling (AD) sensing proposed in literature. Simulation results show that the proposed sensing methods outperform both ED and AD based sensing, especially for the case when the received signal to noise ratio is low.

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SDR implementation of wideband spectrum sensing using machine learning

Sabrina,

Camel,

Djamal

et al. 2024

Int J Communication

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SummaryNew cognitive radio (CR) systems require high throughput and bandwidth. Hence, CR users need to detect wide frequency bands of the radio spectrum to exploit unused frequency channels. This paper proposes a new wideband spectrum sensing (WBSS) detection approach based on machine learning (ML) for scanning subchannels. The originality of the proposed approach is to detect spectrum opportunities using a narrowband spectrum sensing (NBSS) method‐based support vector machine (SVM) classification and two features: energy and goodness of fit (GoF). The simulation results show that the proposed WBSS approach‐based ML presents a higher probability of detection than the WBSS approach‐based conventional detectors, even at low signal‐to‐noise ratio (SNR). Finally, the software defined radio (SDR) implementation validates the proposed WBSS approach for real detection scenarios.

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Spectrum sensing in cognitive radio using goodness of fit testing

Cited by 123 publications

References 17 publications

Distribution of the ratio of an unordered eigenvalue to the trace of a complex central Wishart matrix and its application to cooperative spectrum sensing

Distribution of the ratio of an unordered eigenvalue to the trace of a complex central Wishart matrix and its application to cooperative spectrum sensing

Robust spectrum sensing based on statistical tests

SDR implementation of wideband spectrum sensing using machine learning

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