A Comparison of Energy Detectability Models for Cognitive Radios in Fading Environments

Ciftci, Selami; Torlak, Murat

doi:10.1007/s11277-011-0468-3

Cited by 16 publications

(9 citation statements)

References 13 publications

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“…The SNR is denoted by

δ = \frac{P_{s}}{σ_{w}^{2}}

where Ps is the power of s(n) averaged over N samples; i.e.,

P_{s} = \frac{1}{N} \sum_{n = 1}^{N} true| s ((),, n) true|^{2}

. To generate an intrinsic probability, an energy detector is used where a SU uses N samples from a sensed signal to generate a statistic γ, which is the energy of the received signal:

normalγ = \sum_{n = 1}^{N} true| y ((),, n) true|^{2}

…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…where γ is a realization of a random variable Γ that can be approximated by a Gaussian signal:

normalγ \sim ({,, \begin{matrix} ƒ_{0} ((),, Γ) = normalN ((),, normalN σ_{w}^{2}, 2 normalN σ_{w}^{2}) normalH 0 \\ ƒ_{1} ((),, Γ) = normalN true(normalN σ_{w}^{2} ((),, 1 + normalδ), 2 σ_{w}^{4} ((),, 1 + 2 normalδ) normalH 1 \end{matrix})

…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…where γ is a realization of a random variable Γ that can be approximated by a Gaussian signal: 9,26,27 γ∼…”

Section: Simulation and Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Retracted: New collaborative platform for managing users in using the spectrum bands on cognitive radio networks

Kiani

2016

Int J Numerical Modelling

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Cognitive (or smart) Radio Networks (CRNs) are an innovative approach to wireless engineering in which radios are designed with an unprecedented level of intelligence and agility. The CRNs have imperative component as called spectrum sensing. These networks can enable the secondary users (SUs) for reusing or detecting of idle spectrum bands through this component. This paper proposes a new collaborative platform of this spectrum to employ in resource‐constrained CRNs. In this case, each SU has a certain transmission cost that it is subtracted from a limited resource budget. Deciding and managing of licensed SUs that can transmit without exceeding the resource budget is the paper goal. To handle of a wide variety of resources the proposed schema creates a general platform. These resources can be limited data rate, limited tolerable interference, etc. This platform is based on modeling the CRN as a normal graph. Our simulation results show that the proposed platform has many advantageous features such as achieving fairness amongst SUs, reasonable amount of message exchange, favoring SUs that have reliable connectivity with the Primary User (PU), in addition to high detection rate.

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“…The SNR is denoted by

δ = \frac{P_{s}}{σ_{w}^{2}}

where Ps is the power of s(n) averaged over N samples; i.e.,

P_{s} = \frac{1}{N} \sum_{n = 1}^{N} true| s ((),, n) true|^{2}

. To generate an intrinsic probability, an energy detector is used where a SU uses N samples from a sensed signal to generate a statistic γ, which is the energy of the received signal:

normalγ = \sum_{n = 1}^{N} true| y ((),, n) true|^{2}

…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…where γ is a realization of a random variable Γ that can be approximated by a Gaussian signal:

normalγ \sim ({,, \begin{matrix} ƒ_{0} ((),, Γ) = normalN ((),, normalN σ_{w}^{2}, 2 normalN σ_{w}^{2}) normalH 0 \\ ƒ_{1} ((),, Γ) = normalN true(normalN σ_{w}^{2} ((),, 1 + normalδ), 2 σ_{w}^{4} ((),, 1 + 2 normalδ) normalH 1 \end{matrix})

…”

Section: Simulation and Resultsmentioning

confidence: 99%

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Retracted: New collaborative platform for managing users in using the spectrum bands on cognitive radio networks

Kiani

2016

Int J Numerical Modelling

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“…, the probability of detecting a PU signal on a certain spectrum when it really is present, is as follows (5) where is the time bandwidth product. So, the number of samples needed to meet the required and is derived under approximation by [12].…”

Section: Spectrum Sensingmentioning

confidence: 99%

A slotted ALOHA-Based random access cognitive radio network with capture effect in Rayleigh fading channels

Bayrakdar

Atmaca

Karahan

2013

2013 International Conference on Electronics, Computer and Computation (ICECCO)

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This paper proposes a Slotted ALOHA-Based cognitive radio (CR) network and investigates the throughput of both CR and overall network in the presence of Rayleigh fading. We consider a network that comprises of two different classes of users, primary users (PUs) and CR users. PUs are licensed users and access the channel at any time utilizing Time Division Multiple Access (TDMA) as a Medium Access Control (MAC) technique. CR users are non-licensed users and can access the channel by exploiting Slotted ALOHA as a random access scheme when it is not occupied by the PUs. This study has also revealed that the overall channel utilization can be improved by exploiting the spectrum holes without interfering with the PUs' transmissions.

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“…Furthermore, not matter wh ich aforementioned method is employed, the processing progress is analogue and not easy to implement. In practical, we prefer to handle discrete samples obtained by the sampling law of Nyquist [24][25][26][27]. For the sake of simplicity, we assume the sample rate is 2W and total number of samples is 2 N WW .…”

Section: A General Energy Detection Frameworkmentioning

confidence: 99%

Efficiency-Accuracy Trade-off for Spectrum Sensing in Cognitive Network

Wujian

Dai

Zhang³

et al. 2012

2012 Second International Conference on Instrumentation, Measurement, Computer, Communication and Control

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S pectrum sensing is the first and foremost step in cognitive radio technology, where sensing efficiency and accuracy cannot be simultaneously optimized. Tradeoff between the two metrics is represented by sensing duration and sensing period, which are among the most important parameters in spectrum sensing. Aiming at maximize overall available spectrum opportunity, an efficiency-accuracy tradeoff is proposed with interference to licensed user under specified threshold. Based on Neyman-Pearson criterion, joint optimal mathematical model is established and closed-form expressions are derived. Optimal sensing parameters can be obtained and simulation results verified our proposed scheme .

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A Comparison of Energy Detectability Models for Cognitive Radios in Fading Environments

Cited by 16 publications

References 13 publications

Retracted: New collaborative platform for managing users in using the spectrum bands on cognitive radio networks

Retracted: New collaborative platform for managing users in using the spectrum bands on cognitive radio networks

A slotted ALOHA-Based random access cognitive radio network with capture effect in Rayleigh fading channels

Efficiency-Accuracy Trade-off for Spectrum Sensing in Cognitive Network

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