Local spectrum sensing in non-Gaussian noise

Zahabi, Sayed Jalal; Tadaion, Aliakbar

doi:10.1109/ictel.2010.5478825

Cited by 33 publications

(10 citation statements)

References 16 publications

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“…However, the additive noise may deviate from the traditional Gaussian distribution and in this case it has been widely reported that the additive noise can be modeled by a generalized Gaussian distribution which includes for instance the Gaussian, Laplacian, and uniform distributions as special cases [4]- [6] In this paper, we consider a system with an input signal generated according to a square M-QAM constellation fed to a channel with an Extended Generalized-K (EGK) fading envelope [7], and subject to additive white generalized Gaussian noise (AWGGN). The goal of this paper is to find a generic expression for the probability of error of the system under consideration and then to simplify this expression as much as possible for different values of the fading and noise parameters.…”

Section: Introductionmentioning

confidence: 99%

Exact symbol error probability of square M-QAM signaling over generalized fading channels subject to additive generalized Gaussian noise

Soury

Yılmaz

Alouini

2013

2013 IEEE International Symposium on Information Theory

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Abstract-This paper considers the average symbol error probability of square Quadrature Amplitude Modulation (QAM) coherent signaling over flat fading channels subject to additive generalized Gaussian noise. More specifically, a generic closedform expression in terms of the Fox H function and the bivariate Fox H function is offered for the extended generalized-K fading case. Simplifications for some special fading distributions such as generalized-K fading, Nakagami-m fading, and Rayleigh fading and special additive noise distributions such as Gaussian and Laplacian noise are then presented. Finally, the mathematical formalism is illustrated by some numerical examples verified by computer based simulations for a variety of fading and additive noise parameters.

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Section: Introductionmentioning

confidence: 99%

Exact symbol error probability of square M-QAM signaling over generalized fading channels subject to additive generalized Gaussian noise

Soury

Yılmaz

Alouini

2013

2013 IEEE International Symposium on Information Theory

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“…Implementation of all the detectors for non-Gaussian noise presented in [10], [12], [13] is more complex compared to implementing an energy detector.…”

Section: Introductionmentioning

confidence: 99%

“…Spectrum sensing for cognitive radio networks in the presence of non-Gaussian noise has been addressed by several researchers recently [10], [12], [13]. In [10], a suboptimal L p norm detector for primary signal detection in the presence of non-Gaussian noise was proposed in which a tunable parameter has to be optimized for the underlying type of noise.…”

Section: Introductionmentioning

confidence: 99%

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Polarity-Coincidence-Array Based Spectrum Sensing for Multiple Antenna Cognitive Radios in the Presence of Non-Gaussian Noise

Wimalajeewa

Varshney

2011

IEEE Trans. Wireless Commun.

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ABSTRACT:One of the main requirements of the cognitive radio (CR) systems is the ability to perform spectrum sensing in a reliable manner in challenging environments that arise due to propagation channels which undergo multipath fading and non-Gaussian noise. While most existing literature on spectrum sensing has focused on impairments introduced by additive white Gaussian noise (AWGN), this assumption fails to model the behavior of certain types of noise in practice. In this paper, the use of a non-parametric and easily implementable detection device, namely polarity-coincidence-array (PCA) detector, is proposed for the detection of weak primary signals with a cognitive radio equipped with multiple antennas. Its performance is evaluated in the presence of heavy-tailed noise. The detector performance in terms of the probabilities of detection and false alarm is derived when the communication channels between the primary user transmitter and the multiple antennas at the cognitive radio are AWGN as well as when they undergo Rayleigh fading. From the numerical results, it is observed that a significant performance enhancement is achieved by the PCA detector compared to that of the energy detector with AWGN as well as fading channels as the heaviness of the tail of the non-Gaussian noise increases. KEYWORDS:Cognitive radio, spectrum sensing, non-Gaussian noise, Rayleigh fading, polarity-coincidencearray detectors AbstractOne of the main requirements of the cognitive radio (CR) systems is the ability to perform spectrum sensing in a reliable manner in challenging environments that arise due to propagation channels which undergo multipath fading and non-Gaussian noise. While most existing literature on spectrum sensing has focused on impairments introduced by additive white Gaussian noise (AWGN), this assumption fails to model the behavior of certain types of noise in practice. In this paper, the use of a non-parametric and easily implementable detection device, namely polarity-coincidence-array (PCA) detector, is proposed for the detection of weak primary signals with a cognitive radio equipped with multiple antennas. Its performance is evaluated in the presence of heavy-tailed noise. The detector performance in terms of the probabilities of detection and false alarm is derived when the communication channels between the primary user transmitter and the multiple antennas at the cognitive radio are AWGN as well as when they undergo Rayleigh fading. From the numerical results, it is observed that a significant performance enhancement is achieved by the PCA detector compared to that of the energy detector with AWGN as well as fading channels as the heaviness of the tail of the non-Gaussian noise increases.

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“…Although the Gaussian noise is widely used, the actual additive noise may deviate from it, so a more general noise model is sometimes needed. In fact, it has been widely reported that the generalized Gaussian distribution (GGD) can model different type of noise (see Table I), for example the Gaussian, Laplacian, and uniform distributions are just a special cases of the GGD [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Error Rates of M-PAM and M-QAM in Generalized Fading and Generalized Gaussian Noise Environments

2013

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This letter investigates the average symbol error probability (ASEP) of pulse amplitude modulation and quadrature amplitude modulation coherent signaling over flat fading channels subject to additive white generalized Gaussian noise. The new ASEP results are derived in a generic closed-form in terms of the Fox H function and the bivariate Fox H function for the extended generalized-K fading case. The utility of this new general closed-form is that it includes some special fading distributions, like the Generalized-K, Nakagami-m, and Rayleigh fading and special noise distributions such as Gaussian and Laplacian. Some of these special cases are also treated and are shown to yield simplified results. Index TermsSymbol error probability, pulse amplitude modulation, quadrature amplitude modulation, additive generalized Gaussian noise, Laplacian noise, generalized-K fading, and Nakagami-m fading.

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Local spectrum sensing in non-Gaussian noise

Cited by 33 publications

References 16 publications

Exact symbol error probability of square M-QAM signaling over generalized fading channels subject to additive generalized Gaussian noise

Exact symbol error probability of square M-QAM signaling over generalized fading channels subject to additive generalized Gaussian noise

Polarity-Coincidence-Array Based Spectrum Sensing for Multiple Antenna Cognitive Radios in the Presence of Non-Gaussian Noise

Error Rates of M-PAM and M-QAM in Generalized Fading and Generalized Gaussian Noise Environments

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