Modified criterion of hypothesis testing for signal sensing in cognitive radio

Alamgir, Mohammed; Faulkner, M.; Conder, Phillip; Smith, Peter J.

doi:10.1109/crowncom.2009.5189119

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…In contrast, such a relationship is obtained here as easily as it is in the case of the energydetection approach [20], due to the simplicity of the CDF's given in Theorems 1 and 2.…”

Section: The P D × P M Relationshipmentioning

confidence: 99%

Spectrum Sensing Algorithms via Finite Random Matrix Theory

Abreu

Zhang

Yukitoshi

2011

2011 IEEE International Conference on Communications (ICC)

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We address the Primary User (PU) detection (spectrum sensing) problem, relevant to cognitive radio, from a finite random matrix theoretical (RMT) perspective. Specifically, we employ recently-derived closed-form and exact expressions for the distribution of the standard condition number (SCN) of uncorrelated and semi-correlated random dual Wishart matrices of finite sizes, to design Hypothesis-Testing algorithms to detect the presence of PU signals. An inherent characteristic of the SCN/RMT-based approach, is that no signal-to-noise ratio (SNR) estimation or any other information on the PU signal is required. On top of this property, other attractive advantages of the new techniques are: a) due to the accuracy of the finite SCN distributions, superior performance is achieved under a finite number of samples, compared to asymptotic RMT-based alternatives; b) since expressions to model the SCN statistics both in the absence (H 0) and presence (H1) of PU signal are used, the statistics of the spectrum sensing problem in question is completely characterized; c) as a consequence of a) and b), accurate and simple analytical expressions for the receiver operating characteristic (ROC) -both in terms of P D as a function of P F and in terms of P A as a function of PM -are yielded. It is also shown that the proposed finite RMT-based algorithms outperforms all similar alternatives currently known in the literature, at a substantially lower complexity.

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“…In contrast, such a relationship is obtained here as easily as it is in the case of the energydetection approach [20], due to the simplicity of the CDF's given in Theorems 1 and 2.…”

Section: The P D × P M Relationshipmentioning

confidence: 99%

Spectrum Sensing Algorithms via Finite Random Matrix Theory

Abreu

Zhang

Yukitoshi

2011

2011 IEEE International Conference on Communications (ICC)

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“…Thanks to its robustness against narrow-band and inter-symbol interferences, efficient implementation using FFT and low sensitivity to time synchronization, OFDM is the best candidate for a modern communication system [7]. There are several suboptimal detectors, designed to detect the activity of an OFDM transmitter [8]. Due to the necessity of pilot symbols for channel identification and/or synchronization, some of these detectors detect this known patterns in the received signal [9], which is now applied in some standards.…”

Section: Introductionmentioning

confidence: 99%

Invariant detection of OFDM signals with unknown parameters for cognitive radio applications

Kamalian

Tadaion

2010

IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS

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We propose a computationally efficient spectrum sensing solution for an Orthogonal Frequency Division Multiplexing (OFDM) signal in a frequency selective fading channel with Additive White Gaussian Noise (AWGN). Our assumption is that the data symbols, channel coefficients and the noise variance are all unknown. The nature of the problem leads us to find an invariant detector, the optimum one is Uniformly Most Powerful Invariant (UMPI); our effort shows that this test does not exist, as the final decision statistic depends on some unknown parameters; though, we derive an MPI detector, implanting these parameters, to provide an upper bound for the detection performance. Instead, we develop the Generalized Likelihood Ratio Test (GLRT), substituting the unknown parameters by their Maximum Likelihood (ML) estimates in the Neyman-Pearson likelihood ratio. Furtheremore, we propose a computationally efficient implementation of the resulting detector. Simulation results show a slight decrease in efficiency while gaining so much computational complexity improvement.

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