Energy-based Bayesian spectrum sensing over α-η-μ fading channels

Shobitha, S; Gurugopinath, Sanjeev

doi:10.1109/indicon.2016.7838879

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…The detector is defined using the Bayesian approach [27,48], which combines the probabilities of the two wrong decisions P F A and P M D through using the a priori probability p(H 0 ) of having a spectrum free from PU transmissions and the threshold value. This work is further extended in [43] which considers a generalized fading model termed α − κ − µ, which fits experimental measurements well. The study in [44] allows for Nakagami fading, Gaussian noise and shadowing, while the received signal follows a Gamma distribution because the detector works for very few signal samples (such that cannot be assumed to have Gaussian distribution).…”

Section: Energy Detectorsmentioning

confidence: 93%

“…Proposed modifications are related to the dynamic readjustment of the decision threshold, the comparison of its performance with other sensing methods, the received signal's decomposition into its frequency components, finding the optimal sensing time against the a priori probability of the spectrum being available p(H 0 ). Studies such as those presented in [33,[41][42][43][44][45][46] introduce an important dimension to the design of ED, namely, they add more realistic propagation conditions to the detector's analytical model. The method proposed in [33] identifies the PU signal under Rayleigh fading and unknown variance of the Gaussian-distributed noise, and in addition to the probability of detection, the spectrum utilization coefficient 2 is evaluated.…”

Section: Energy Detectorsmentioning

confidence: 99%

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Feature Extraction in Local SpectrumSensing for Next Generation CognitiveRadios � A Review

Ivanov

2020

JMM

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In the context of the currently developed networks for the realization of future communications, the concept of Cognitive Radio (CR) has a significant place. Its implementation in modern ultra-dense networks (UDN) requires the development of novel and improved adaptive solutions for the main functionalities of a CR system. This technology has a great potential for solving the significant spectrum underutilization issue which has been established as characteristic for the traditional communication networks and hence, the continual and substantive research efforts in the recent years. The principal challenge for CRs is the optimization of spectrum utilization without creating unwanted interference for the incumbent (primary) users. Thus, a significant portion of the research is directed towards the vital spectrum sensing functionality. This paper reviews the potential uses of CR in UDN as well as the main approaches for modeling of spectrum sensing with respect to signal detection. The review focuses on local spectrum sensing techniques and presents the strengths and weaknesses of their mathematical definition. Specific attention is given to the channel models which are considered in the literature and to what kinds of features are extracted from the received signal to achieve accurate detection.

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Section: Energy Detectorsmentioning

confidence: 93%

Section: Energy Detectorsmentioning

confidence: 99%

Feature Extraction in Local SpectrumSensing for Next Generation CognitiveRadios � A Review

Ivanov

2020

JMM

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An integrated performance evaluation of ED‐based spectrum sensing over α−κ−μ and α−κ−μ‐Extreme fading channels

Rasethuntsa

Kumar

2019

Trans Emerging Tel Tech

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This paper presents a comparative performance evaluation of energy detector (ED) under the assumption that the propagation signal experiences fading modeled by the nonlinear generalized α−κ−μ or α−κ−μ‐Extreme fading channels. Novel closed‐form analytical expressions for the average probability of detection and the average area under the receiver operating characteristic (ROC) curve (AUC) are derived in terms of the now highly prevalent bivariate Fox H‐function under single‐user spectrum sensing, cooperative spectrum sensing, and square law selection diversity reception. The results derived in the present work are generic as both channel models are compound distributions containing as special cases several other well‐known fading models such as the Nakagami‐m, Rice, the one‐sided Gaussian distribution, and the Weibull distribution, among others. The performance of ED is evaluated by employing the traditional ROC and AUC curves over additive white Gaussian noise by varying model parameters and observing system performances. Analytical results are verified via Monte Carlo simulations in MATLAB®.

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Optimization Analysis of Cooperative Spectrum Sensing System over Generalized $$\kappa -\mu $$ and $$\eta -\mu $$ Fading Channels

Balam

Siddaiah

Nallagonda³

2020

Wireless Pers Commun

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Energy-based Bayesian spectrum sensing over α-η-μ fading channels

Cited by 3 publications

References 25 publications

Feature Extraction in Local SpectrumSensing for Next Generation CognitiveRadios � A Review

Feature Extraction in Local SpectrumSensing for Next Generation CognitiveRadios � A Review

An integrated performance evaluation of ED‐based spectrum sensing over α−κ−μ and α−κ−μ‐Extreme fading channels

Optimization Analysis of Cooperative Spectrum Sensing System over Generalized $$\kappa -\mu $$ and $$\eta -\mu $$ Fading Channels

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