A Performance Comparison Of Spectrum Sensing Exploiting Machine Learning Algorithms

Nimudomsuk, Pongphat; Sanguanwattanaraks, Montakan; Srisomboon, Kanabadee; Lee, Wilaiporn

doi:10.1109/ecti-con51831.2021.9454875

Cited by 5 publications

(8 citation statements)

References 9 publications

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“…This indicates that σ * that maximizes the system accuracy for a fixed n is given in (9), while the condition ( 10) is satisfied. If the parameters do not meet the limitations provided in (10), the accuracy expression will become a monotonic function and the maximum accuracy is obtained when…”

Section: B Accuracy Analysismentioning

confidence: 99%

“…A search algorithm can be then developed to solve the original integer programming problem P1. Firstly, for n = 1 : N , we first calculate the σ * by using (9) for each cluster size n if the parameters meet the limitations provided in (10). The first pair of (n, σ * ) that meets the accuracy requirement Q(n, σ) ≥ can be found.…”

Section: Search Algorithmmentioning

confidence: 99%

“…The minimum cluster size required and its optimal threshold are therefore given by (n, σ * ). On the other hand, if the parameters do not meet the limitations provided in (10), the optimal threshold is σ m and σ m = n or σ m = n+1 2 . The minimum cluster size required and its optimal threshold are therefore given by (n, σ m ).…”

Section: Search Algorithmmentioning

confidence: 99%

“…Particular attention has been drawn to cognitive radio networks (CRNs) since they can greatly improve the spectrum utilization by allowing secondary users (SUs) to share the licensed spectrum with primary users (PUs) [3]- [5]. Cognitive radios sense the surrounding radio frequency (RF) environment and make decisions as an intelligent wireless communication system [6]- [10]. More specifically, to improve the sensing accuracy, cooperative sensing (CS) can be utilized that requires multiple SUs to sense together and fuses all the sensing results at a fusion center (FC).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimization for Prediction-Driven Cooperative Spectrum Sensing in Cognitive Radio Networks

Nie

et al. 2022

2022 IEEE International Conference on Communications Workshops (ICC Workshops)

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Empirical studies have observed that the spectrum usage in practice follows regular patterns. Machine learning (ML)-based spectrum prediction techniques can thus be used jointly with cooperative sensing in cognitive radio networks (CRNs). In this paper, we propose a novel cluster-based sensing-after-prediction scheme and aim to reduce the total energy consumption of a CRN. An integer programming problem is formulated that minimizes the cluster size and optimizes the decision threshold, while guaranteeing the system accuracy requirement. To solve this challenging optimization problem, the relaxation technique is used which transforms the optimization problem into a tractable problem. The solution to the relaxed problem serves as a foundation for the solution to the original integer programming. Finally, a low-complexity search algorithm is proposed which achieves the global optimum, as it obtains the same performance with exhaustive search. Simulation results demonstrate that the total energy consumption of CRN is greatly reduced by applying our clustered sensing-after-prediction scheme.

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Section: B Accuracy Analysismentioning

confidence: 99%

Section: Search Algorithmmentioning

confidence: 99%

Section: Search Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimization for Prediction-Driven Cooperative Spectrum Sensing in Cognitive Radio Networks

Nie

et al. 2022

2022 IEEE International Conference on Communications Workshops (ICC Workshops)

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“…Spectrum sensing algorithms based on machine learning have been widely studied. Among them, in literature [6], the results demonstrate that when the threshold is unclear, the neural network has the highest detection probability and the shortest detection time among three machine learning methods (logistic regression, KNN, and neural network). In [7], energy detection and cyclic stationary feature detection are combined, and signal samples are trained on a neural network created artificially.…”

Section: Introductionmentioning

confidence: 99%

CNN-SVM Spectrum Sensing in Cognitive Radio Based on Signal Covariance Matrix

Wang

Guo

2022

J. Phys.: Conf. Ser.

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Among the basic components of cognitive radio technology is spectrum sensing technology. Cooperative spectrum sensing is also a crucial factor research directions of spectrum sensing. Due to the problems of channel fading and hidden nodes, the spectrum sensing based on a single node cannot meet the requirements of the cognitive radio system for the primary user (PU) detection performance. A spectrum sensing algorithm is based on CNN-SVM and covariance matrix. In this algorithm, the covariance matrix of the signal is input into the convolutional neural network for feature extraction. Secondly, the result of the fully connected layer is taken as the input of the support vector machine for training to obtain a classifier, and finally, spectrum sensing is performed. The simulation outcomes demonstrate that the algorithm suggested in this research works outperform other algorithms in this paper. When the signal-to-noise ratio is -14 dB, the false alarm probability is 0.1, the number of secondary users is 32, and the detection probability reaches 0.81.

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Spectrum sensing with multihop distributed switch and stay combining

Alnwaimi

2021

SIViP

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A Performance Comparison Of Spectrum Sensing Exploiting Machine Learning Algorithms

Cited by 5 publications

References 9 publications

Optimization for Prediction-Driven Cooperative Spectrum Sensing in Cognitive Radio Networks

Optimization for Prediction-Driven Cooperative Spectrum Sensing in Cognitive Radio Networks

CNN-SVM Spectrum Sensing in Cognitive Radio Based on Signal Covariance Matrix

Spectrum sensing with multihop distributed switch and stay combining

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