Eigenvalue-based reliable spectrum sensing scheme for cognitive radio networks

Charan, Chhagan; Pandey, Rajoo

doi:10.1109/icnte.2017.7947969

Cited by 10 publications

(6 citation statements)

References 10 publications

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“…They transfer the weighting issue of eigenvalue to the energy based detection and originate the theoretical expressions of detection threshold. Charan et al [11] have projected the optimum number of samples for given SNR and also presented the theoretical analysis and numerical formula for optimum samples. Random matrix theory is used in [12] to compute the Eigen ratio and threshold for the detection method depending on sample covariance matrix of received signal.…”

Section: Related Workmentioning

confidence: 99%

“…The same range and samples are specified in this paper for Nakagami fading channel and analyse the performance of the detection of the primary user. In paper [11], the author utilizes the second SNR range for Nakagami fading channel but the samples are 200 to 2000. At the same time, we anticipate the second SNR range for observing the probability of detection of primary user in high SNR.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Spectrum Prediction in Cognitive Radio Network Using Machine Learning Techniques

Arivudainambi¹,

Mangairkarasi²,

Kumar³

2022

Intelligent Automation &Amp; Soft Computing

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Cognitive Radio (CR) aims to achieve efficient utilization of scarcely available radio spectrum. Spectrum sensing in CR is a basic process for identifying the existence or absence of primary users. In spectrum sensing, CR users suffer from deep fading effects and it requires additional sensing time to identify the primary user. To overcome these challenges, we frame Spectrum Prediction-Channel Allocation (SP-CA) algorithm which consists of three phases. First, clustering mechanisms to select the spectrum coordinator. Second, Eigenvalue based detection method to expand the sensing accuracy of the secondary user. Third, Bayesian inference approach to reduce the performance degradation of the secondary user. The Eigenvalue based detection method is compared with Energy detection method in terms of varying false alarm rates and samples. The Eigenvalue detection method achieves better performance than Energy detection method. The Simulation results show that our approach gives better performance in terms of reducing sensing time and increasing sensing accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Spectrum Prediction in Cognitive Radio Network Using Machine Learning Techniques

Arivudainambi¹,

Mangairkarasi²,

Kumar³

2022

Intelligent Automation &Amp; Soft Computing

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“…Different statistical-based detection techniques are proposed in the literature. Cyclostationary Feature Detection (CFD) [25], Eigenvalue [26], Autocorrelation-Based spectrum sensing scheme [27]. All these schemes share the same advantage of being insensitive to noise power levels and higher performance.…”

Section: Noise Uncertainty In Cognitive Radio Systemsmentioning

confidence: 99%

Double Threshold Weighted Energy Detection for Asynchronous PU Activities in the Presence of Noise Uncertainty

2020

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Cognitive Heterogeneous Network (CHN) is a network that provides heterogeneous services on a cognitive radio basis. The existence of such heterogeneous services introduces a syndrome of concurrent problems. Handling each problem separately may result in more consequences. This paper aims to mitigate the combined effects of noise uncertainty and asynchronous primary user occurrence within the sensing interval of the secondary user in cognitive heterogeneous network. The paper proposes a double threshold energy detection method based on unequal scale sampling criteria. An analytic formula is deduced for both thresholds employing the power scaling criteria. Simulation results show a complete agreement with the performance expectations in terms of increased probability of detection while keeping the false alarm probability within the required limits, which is guaranteed by plotting the ROC curves. Moreover, an optimal threshold is formulated to minimize the overall detection errors, that led to further enhancements in performance as illustrated also via simulations.

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“…The EBD approach is applied in [39,58] but instead of the standard assumption for the threshold equal to one, it is derived by the CFAR method. In addition, a variant of the test statistics is introduced in which the average signal power is divided by δ min (Energy to Minimum Eignevalue, EME).…”

Section: Eigenvalue-based Detectorsmentioning

confidence: 99%

“…In addition, a variant of the test statistics is introduced in which the average signal power is divided by δ min (Energy to Minimum Eignevalue, EME). Authors in [58] develop an optimal threshold for guaranteed values of both P D and P F A and specific N (which is a function of the SNR). Gaussian noise is assumed.…”

Section: Eigenvalue-based Detectorsmentioning

confidence: 99%

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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Eigenvalue-based reliable spectrum sensing scheme for cognitive radio networks

Cited by 10 publications

References 10 publications

Spectrum Prediction in Cognitive Radio Network Using Machine Learning Techniques

Spectrum Prediction in Cognitive Radio Network Using Machine Learning Techniques

Double Threshold Weighted Energy Detection for Asynchronous PU Activities in the Presence of Noise Uncertainty

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

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