On the Energy Detection of Unknown Signals Over Fading Channels

Digham, F.F.; Alouini, Mohamed‐Slim; Simon, M.K.

doi:10.1109/tcomm.2006.887483

Cited by 1,705 publications

(406 citation statements)

References 6 publications

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“…In practice, the estimated noise power may be different from the actual noise power [9,15]. For this reason, some uncertainties are in question at the calculation of the noise power.…”

Section: Simulationmentioning

confidence: 99%

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Covariance Based Spectrum Sensing with Studentized Extreme Eigenvalue

Çiflikli

Ilgin

2018

Teh. vjesn.

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Abstract:The eigenvalue based detection is a low-cost spectrum sensing method that detects the presence of primary user signal at a desired frequency. In this study, the largest eigenvalue distribution used in eigenvalue based detection methods is expressed using a new centering and scaling coefficients adjustment. Thus, the detection probability (Pd) and false detection probability (Pfa) equations for the maximum-minimum eigenvalue (MME), maximum eigenvalue to trace (MET) and maximum eigenvalue-geometric mean (ME-GM) have been obtained again. Weibull fading channels are the best model for wireless communication. For this reason, the studies were simulated in Weibull fading channels and analysed in detail with receiver operating characteristic curves (ROC). The results were compared with traditional methods and found to be more accurate.

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“…In practice, the estimated noise power may be different from the actual noise power [9,15]. For this reason, some uncertainties are in question at the calculation of the noise power.…”

Section: Simulationmentioning

confidence: 99%

“…Different methods such as matched filter [7,8], energy detection [9,10], cyclostationary detection [11][12][13][14], eigenvalue detection [15][16][17], and covariance-based detection [18] are proposed for spectrum sensing in the literature. These methods have their own advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Covariance Based Spectrum Sensing with Studentized Extreme Eigenvalue

Çiflikli

Ilgin

2018

Teh. vjesn.

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“…The study in [26] demonstrates that energy detection is able to deliver satisfactory results with cooperative sensing and sometimes even outperforms feature detection-based approaches. Accordingly, energy detection can serve as an inexpensive yet effective spectrum-sensing technique that is the most commonly adopted approach for spectrum sensing [20,25,26,33,34].…”

Section: Energy Detectionmentioning

confidence: 99%

Adaptive Communication Techniques for the Internet of Things

Roussos

2013

JSAN

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Abstract:The vision for the Internet of Things (IoT) demands that material objects acquire communications and computation capabilities and become able to automatically identify themselves through standard protocols and open systems, using the Internet as their foundation. Yet, several challenges still must be addressed for this vision to become a reality. A core ingredient in such development is the ability of heterogeneous devices to communicate adaptively so as to make the best of limited spectrum availability and cope with competition which is inevitable as more and more objects connect to the system. This survey provides an overview of current developments in this area, placing emphasis on wireless sensor networks that can provide IoT capabilities for material objects and techniques that can be used in the context of systems employing low-power versions of the Internet Protocol (IP) stack. The survey introduces a conceptual model that facilitates the identification of opportunities for adaptation in each layer of the network stack. After a detailed discussion of specific approaches applicable to particular layers, we consider how sharing information across layers can facilitate further adaptation. We conclude with a discussion of future research directions.

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“…The energy detection with square law combining is studied in (Digham et al, 2007). In this scheme, the outputs of the square-and-integrate devices are combined which in turn gives a new decision statistic.…”

Section: Energy Detection Over Nakagami Fading Channelsmentioning

confidence: 99%

“…The detection and false alarm probabilities are given as (8) and (9) (Digham et al, 2007), which are obtained under L independent Rayleigh branches: …”

Section: Energy Detection Over Nakagami Fading Channelsmentioning

confidence: 99%

Review of Energy Detection for Spectrum Sensing in Various Channels and Its Performance for Cognitive Radio Applications

M.¹

2012

American Journal of Engineering and Applied Sciences

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Spectrum sensing is the basic and important operation in Cognitive Radio (CR) to find the unused spectrum. Energy detector is a popular sensing method because it doesn't require transmitted signal properties, channel information, of even the type of modulation. This study summarizes the performance result of energy detector over Additive White Gaussian Noise (AWGN), Rayleigh fading and Nakagami fading channels. Energy detection with soft decision and hard decision are also studied for different number of cognitive nodes as well as each cognitive node having multiple antennas. The performance of hard decision and soft decision are evaluated by means of complementary Receiver Operating Characteristic (ROC) curves. It clearly shows that the probability of missing detection decreases for increasing the number of antennas in cognitive node and also increasing of cooperated cognitive users.

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On the Energy Detection of Unknown Signals Over Fading Channels

Cited by 1,705 publications

References 6 publications

Covariance Based Spectrum Sensing with Studentized Extreme Eigenvalue

Covariance Based Spectrum Sensing with Studentized Extreme Eigenvalue

Adaptive Communication Techniques for the Internet of Things

Review of Energy Detection for Spectrum Sensing in Various Channels and Its Performance for Cognitive Radio Applications

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