Two-Stage Spectrum Sensing Using Fuzzy Logic for Cognitive Radio Networks

Ahuja, Bhawna; Kaur, Gurjit

doi:10.1007/s40010-019-00595-7

Cited by 9 publications

(4 citation statements)

References 16 publications

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“…A marginal change in the noise uncertainty factor ρ deteriorates the performance quickly. https://www.indjst.org/ The method proposed in (16) to address the problem of noise uncertainty are simulated and compared with the single threshold using the constant false alarm rate. From Figures 7 and 8 , it is observed that the PU detection using single threshold fails as the noise uncertainty increases, because the threshold fixed is independent of the noise variance.…”

Section: Resultsmentioning

confidence: 99%

“…From Figures 7 and 8 , it is observed that the PU detection using single threshold fails as the noise uncertainty increases, because the threshold fixed is independent of the noise variance. Realizing Eqn (10)(11)(12)(13)(14)(15)(16)(17) in MATLAB and by appropriately choosing the membership functions and the fuzzy rule base in the fuzzy inference system the proposed adaptive thresholding using fuzzy logic method for ED under Laplacian noise, provides reliable detection compared to the other methods when the noise uncertainty increases. Another interesting observation drawn is on the improvement in signal detection in the low SNR regimes.…”

Section: Resultsmentioning

confidence: 99%

“…Through analytical and simulated results, it is shown that this method is robust to dynamically varying noise (8) . In (9)(10)(11) fuzzy logic is used to address the decision on the PU activity. This scheme showed better detection performance in low signal to noise ratio (SNR) regions.…”

Section: Introductionmentioning

confidence: 99%

“…• To overcome the effect of noise uncertainty, the spectrum sensing algorithm using adaptive fuzzy threshold proposed in (16) primarily studied under Gaussian noise is investigated under non-Gaussian noise environment. • The effect on the probability of detection due to the characterization of the statistical moments in the noise distribution is investigated.…”

Section: Introductionmentioning

confidence: 99%

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Spectrum Sensing in Non-Gaussian Noise

Reddy¹,

Ravinder²

2021

IJST

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Background: Spectrum sensing is a crucial step to realize the Cognitive Radio technology. The spectrum sensing schemes at low signal-to-noise ratio, noise uncertainty and especially under the background of non-Gaussian noise, provide low detection of the primary user. This results in missed detection or false alarm and increases higher interference to the primary user. Objectives: Detection schemes designed for additive Gaussian noise exhibit poor performance in the non-Gaussian environment. This study considers the problem of spectrum sensing with the assumption that the noise follows a non-Gaussian distribution with heavier tails. Methods/findings: A fuzzy logicbased method is proposed for primary user detection under non Gaussian Noise. The results are highlighted for the Laplacian noise. Through Monte Carlo simulations it is observed that Laplacian noise noticeably affects the performance of energy detector. Also, a fractional change in noise uncertainty degrades the performance of energy detector. The performance of the proposed scheme is presented through receiver operating characteristic (ROC) and plot of the detection probability versus signal-to-noise ratio (SNR) using simulations. It is shown that by appropriately choosing the membership functions and the fuzzy rule base in the fuzzy inference system the proposed fuzzy logic method for spectrum sensing provides reliable detection.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spectrum Sensing in Non-Gaussian Noise

Reddy¹,

Ravinder²

2021

IJST

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Arithmetic Optimization Algorithm Quantization Optimized With Energy Detection Using Nonparametric Amplitude

T M

et al. 2024

Int J Communication

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The spectrum sensing is a major significant task in cognitive radio networks (CRNs) to avoid the unacceptable interference to primary users (PUs). Here, the threshold value determines the effectiveness of spectrum sensing and regarded as a sensing system. The fixed threshold used by the current energy detection‐based spectrum sensing (SS) techniques does not provide sufficient safety for the main users. The threshold is determined by lowering the complete probability of decision error in addition to these guidelines. Therefore, an energy detection using nonparametric amplitude quantization optimized with arithmetic optimization algorithm for enhanced spectrum sensing in CRNs (ED‐NAQ‐AOA‐SS CRN) is proposed in this paper to acquire the ideal threshold for decreasing the total error probability. The proposed method achieves greater probability of detection of 99.67%, 98.38%, 92.34%, and 97.45%, lower settling time of 98.33%, 89.34%, 83.12%, and 88.96%, and lower error rate of 93.15%, 91.25%, 79.90%, and 92.88% compared with existing techniques, like intelligent spectrum sharing and sensing in CRN with adaptive rider optimization algorithm (AROA), a novel technique for spectrum sensing in CRN utilizing fractional gray wolf optimization with the cuckoo search optimization (GWOCS), and adaptive neuro‐fuzzy inference scheme depending on cooperative spectrum sensing optimization in CRNs (ANFIS).

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Optimal Relay Node Selection Using Multi-Objective based Pity Beetle Optimization Algorithm for Cognitive Radio Networks

Kalaimagal

Vasanthi

2021

Wireless Pers Commun

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Two-Stage Spectrum Sensing Using Fuzzy Logic for Cognitive Radio Networks

Cited by 9 publications

References 16 publications

Spectrum Sensing in Non-Gaussian Noise

Spectrum Sensing in Non-Gaussian Noise

Arithmetic Optimization Algorithm Quantization Optimized With Energy Detection Using Nonparametric Amplitude

Optimal Relay Node Selection Using Multi-Objective based Pity Beetle Optimization Algorithm for Cognitive Radio Networks

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