Cooperative spectrum sensing optimization based adaptive neuro-fuzzy inference system (ANFIS) in cognitive radio networks

Mabrook, M. Mourad; Taha, Hussein A.; Hussein, Aziza I.

doi:10.1007/s12652-020-02121-9

Cited by 14 publications

(6 citation statements)

References 40 publications

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“…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%

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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%

“…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%

Spectrum Sensing in Non-Gaussian Noise

Reddy¹,

Ravinder²

2021

IJST

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“…2. The Maximum Energy Efficiency Estimator block is firstly supplied by the number of active users (Ibrahim Salah et al 2021;Mabrook et al 2019) served by the cell (Zhong et al 2020) using the base ( 4) station database or using spectrum sensing techniques (Mabrook et al 2019(Mabrook et al , 2020. Then, Eq.…”

Section: Impact Of Multiple Bs Antennas and Usersmentioning

confidence: 99%

Energy efficiency optimization in adaptive massive MIMO networks for 5G applications using genetic algorithm

et al. 2022

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Given that, the exponential pace of growth in wireless traffic has continued for more than a century, wireless communication is one of the most influential innovations in recent years. Massive Multiple-Input Multiple-Output (M-MIMO) is a promising technology for meeting the world's exponential growth in mobile data traffic, particularly in 5G networks. The most critical metrics in the massive MIMO scheme are Spectral Efficiency (SE) and Energy Efficiency (EE). For single-cell M-MIMO uplink transmission, energy and spectral-efficiency trade-offs have to be estimated by optimizing the number of base station antennas versus the number of active users. This paper proposes an adaptive optimization technique focusing on maximizing Energy Efficiency at full spectral efficiency using a Genetic Algorithm (GA) optimizer. The number of active antennas is determined according to the change in the number of active users based on the proposed GA scheme that optimizes the EE in the M-MIMO system. Simulation results show that the GA optimization technique achieved the maximum energy efficiency of the 5G M-MIMO platform and the maximum efficiency in the trade-off process.

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“…The block diagram of the proposed system is depicted in Figure (2). The Maximum Energy Efficiency Estimator block is firstly supplied by the number of active users [18,19] served by the cell [20] using the base station database or using spectrum sensing techniques [18,22]. Then, equation ( 4) is used to estimate Max.…”

Section: Fig2 Block Diagram Of the Proposed Adaptive Antenna Selection Systemmentioning

confidence: 99%

“…In order to get the optimum number of massive MIMO antennas corresponding to the active users served by 5G cell, many optimization techniques are proposed [ 21]. However, GA optimization technique is mostly enrolled in massive MIMO networks as [22]. GA technique supports selection, crossover, and mutation processes.…”

Section: Ee Optimization Based On Genetic Algorithm Artificial Intelligencementioning

confidence: 99%

Energy Efficiency Optimization in Adaptive Massive MIMO Networks for 5G Applications Using Genetic Algorithm

Ibrahim

Mabrook

Rahouma

et al. 2021

Preprint

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Given that the exponential pace of growth in wireless traffic has continued for more than a century, wireless communication is one of the most influential innovations in recent years. Massive Multiple-Input Multiple-Output (M-MIMO) is a promising technology for meeting the world's exponential growth in mobile data traffic, particularly in 5G networks. The most critical metrics in the massive MIMO scheme are Spectral Efficiency (SE) and Energy Efficiency (EE). For single-cell MMIMO uplink transmission, energy and spectral-efficiency trade-offs have to be estimated by optimizing the number of base station antennas versus the number of active users. This paper proposes an adaptive optimization technique focusing on maximizing Energy Efficiency at full spectral efficiency using a Genetic Algorithm (GA) optimizer. The number of active antennas is estimated according to the change in the number of active users based on the proposed GA scheme that optimizes the EE in the M-MIMO system. Simulation results show that the GA optimization technique achieved the maximum energy efficiency of the 5G M-MIMO platform and the maximum efficiency in the trade-off process.

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Cooperative spectrum sensing optimization based adaptive neuro-fuzzy inference system (ANFIS) in cognitive radio networks

Cited by 14 publications

References 40 publications

Spectrum Sensing in Non-Gaussian Noise

Spectrum Sensing in Non-Gaussian Noise

Energy efficiency optimization in adaptive massive MIMO networks for 5G applications using genetic algorithm

Energy Efficiency Optimization in Adaptive Massive MIMO Networks for 5G Applications Using Genetic Algorithm

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