Generalized Energy Detection Under Generalized Noise Channels

Miridakis, Nikolaos I.; Tsiftsis, Theodoros A.; Yang, Guanghua

doi:10.1109/lwc.2020.3009677

Cited by 6 publications

(5 citation statements)

References 15 publications

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“…However, in a practical scenario, channel noise is not strictly Gaussian and follows a heavy-tailed nature. To address this challenge, channel noise is noted to be modelled following a Laplacian distribution [15][16][17]. One can note, the GGD family of distribution is characterised by a symmetric unimodal density characteristic and can support a variable tail-length based on the shape parameter, β of GGD [33].…”

Section: Motivationmentioning

confidence: 99%

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Norm‐based spectrum sensing for cognitive radios under generalised Gaussian noise

et al. 2023

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Cognitive radio (CR) systems are configured to dynamically assess the spectrum utilisation and contribute towards an improved spectrum efficiency. Hence, accurate detection of the incumbent signal in a given channel, popularly known as spectrum sensing (SS), is essential for CR. Here, in the domain of SS, the authors introduce a new goodness‐of‐fit test (GoFT) founded on p‐norm of the observations at the receiver node. To capture the heavy‐tailed nature of noise distribution in practical communication channels, the authors utilise generalised Gaussian distribution (GGD) as a noise model. A novel p‐norm detector (PND) and a geometric power detector (GPD) is proposed and corresponding probability density function (PDF) under GGD is derived. Via Monte Carlo simulations, the authors show a match of the derived PDFs with the simulation results. Using Neyman‐Pearson framework the performances of PND and GPD are compared with an existing differential entropy detector (DED), the well‐known energy detector (ED) and joint correlation and energy detector (CED) under GGD noise model. Evaluation of proposed PND and GPD utilising Monte Carlo simulations indicate a superior performance. Further, the experiments employing real‐world data establish superiority of the proposed detectors as compared to existing techniques. The authors derive and implement an optimised threshold for PND, providing further improvement in performance.

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Section: Motivationmentioning

confidence: 99%

“…Additionally, they do not evaluate the performance of their proposed detector under GGD. Authors in ref [16] work on IED under Rician fading and model the channel noise using Mcleish distribution. However, this work is limited to the study of IED [8] and does not calculate the p ‐norm of the received samples.…”

Section: Introductionmentioning

confidence: 99%

Norm‐based spectrum sensing for cognitive radios under generalised Gaussian noise

et al. 2023

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“…Many detectors are based on this criterion, the most known is the traditional ED [ 30 , 31 ]. Other detectors such as the Cumulative Power Spectral Density (CPSD) detector [ 29 ], cyclo-energy detector [ 32 ] and generalized ED [ 33 , 34 , 35 ] are based on differentiating between the energy of the received signal with and without the presence of PU’s signal. It is worth mentioning that the generalized ED may use a power exponent

in the definition of it as an extension of the ED Test Statistic, which is based on the energy of the received signal (i.e.,

).…”

Section: Half-duplex Cognitive Radio: Listen Before Talkmentioning

confidence: 99%

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

Nasser

Hassan

Chaaya

et al. 2021

Sensors

137

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Spectrum Sensing (SS) plays an essential role in Cognitive Radio (CR) networks to diagnose the availability of frequency resources. In this paper, we aim to provide an in-depth survey on the most recent advances in SS for CR. We start by explaining the Half-Duplex and Full-Duplex paradigms, while focusing on the operating modes in the Full-Duplex. A thorough discussion of Full-Duplex operation modes from collision and throughput points of view is presented. Then, we discuss the use of learning techniques in enhancing the SS performance considering both local and cooperative sensing scenarios. In addition, recent SS applications for CR-based Internet of Things and Wireless Sensors Networks are presented. Furthermore, we survey the latest achievements in Spectrum Sensing as a Service, where the Internet of Things or the Wireless Sensor Networks may play an essential role in providing the CR network with the SS data. We also discuss the utilisation of CR for the 5th Generation and Beyond and its possible role in frequency allocation. With the advancement of telecommunication technologies, additional features should be ensured by SS such as the ability to explore different available channels and free space for transmission. As such, we highlight important future research axes and challenging points in SS for CR based on the current and emerging techniques in wireless communications.

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“…In [9], the generalized energy detector (GED) is analytically studied when impaired by generalized noise with McLeish distribution. Particular cases of the closed form expressions and the noise model given in [9] can also be used to address the performance of the AVC and the ED, which are especial cases of the GED, over Gaussian and Laplacian noise. The SNR w is not addressed in [9].…”

Section: A Related Workmentioning

confidence: 99%

“…Particular cases of the closed form expressions and the noise model given in [9] can also be used to address the performance of the AVC and the ED, which are especial cases of the GED, over Gaussian and Laplacian noise. The SNR w is not addressed in [9].…”

Section: A Related Workmentioning

confidence: 99%

Simple SNR Wall Calculation by Equating the Medians of the Detector's Test Statistic

Guimarães¹

2022

Anais Do XL Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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An apparently definitive conclusion that can be drawn from the literature is that the calculation of the signal-tonoise ratio wall (SNRw) of detectors for spectrum sensing is not trivial. Conventionally, to make this calculation one has to find the expressions of the probabilities of detection and false alarm, and of the required number of samples to achieve target probabilities under worst-case noise uncertainty. However, a simple calculation of the SNRw can be devised based on a theorem stating that the existence of an SNRw requires that the test statistics have overlapping medians under the two test hypotheses. Grounded on this theorem, in this paper it is devised such a simple calculation method, which is applied to find the SNRw of the absolute value cumulating (AVC) detector and the energy detector (ED) under Gaussian and Laplacian noise. Simulation results are presented to support and complement the analytical findings.

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Generalized Energy Detection Under Generalized Noise Channels

Cited by 6 publications

References 15 publications

Norm‐based spectrum sensing for cognitive radios under generalised Gaussian noise

Norm‐based spectrum sensing for cognitive radios under generalised Gaussian noise

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

Simple SNR Wall Calculation by Equating the Medians of the Detector's Test Statistic

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