An Optimized Spectrum Sensing Implementation Based on SVM, KNN and TREE Algorithms

Mohammed, Saber; Rharras, Abdessamad El; Saadane, Rachid; Kharraz, Aroussi Hatim; Chehri, Abdellah

doi:10.1109/sitis.2019.00068

Cited by 21 publications

(10 citation statements)

References 20 publications

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“…Intelligent spectrum sensing can find available spectrum resources in both space and time dimensions, effectively improving spectrum utilization efficiency. The combination of the energy detection method with support vector machines (SVM) in Saber et al [23] is well validated in Bicaïs et al [24]. This paper combines the energy detection method with a neural network and proposes an ANN-based energy detection method (ANN-ED), as shown in Fig.…”

Section: Ann-based Multi-node Inter-satellite Spectrum Sensing Algorithmmentioning

confidence: 92%

An ANN-based Intelligent Spectrum Sensing Algorithm for Space-based Satellite Networks

2023

KSII TIIS

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In Low Earth Orbit (LEO) satellite networks, satellites operate fast and the inter-satellite link change period is short. In order to sense the spectrum state in LEO satellite networks in realtime, a space-based satellite network intelligent spectrum sensing algorithm based on artificial neural network (ANN) is proposed, while Geosynchronous Earth Orbit (GEO) satellites are introduced to make fast and effective judgments on the spectrum state of LEO satellites by using their stronger arithmetic power. Firstly, the visibility constraints between LEO satellites and GEO satellites are analyzed to derive the inter-satellite link building matrix and complete the inter-satellite link situational awareness. Secondly, an ANN-based energy detection (ANN-ED) algorithm is proposed based on the traditional energy detection algorithm and artificial neural network. The ANN module is used to determine the spectrum state and optimize the traditional energy detection algorithm. GEO satellites are used to fuse the information sensed by LEO satellites and then give the spectrum decision, thereby realizing the inter-satellite spectrum state sensing. Finally, the sensing quality is evaluated by the analysis of sensing delay and sensing energy consumption. The simulation results show that our proposed algorithm has lower complexity, the sensing delay and sensing energy consumption compared with the traditional energy detection method.

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Section: Ann-based Multi-node Inter-satellite Spectrum Sensing Algorithmmentioning

confidence: 92%

An ANN-based Intelligent Spectrum Sensing Algorithm for Space-based Satellite Networks

2023

KSII TIIS

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“…The k-Nearest Neighbors algorithm is a supervised machine learning framework that can be used to solve various problems related to classification and regression [24]. In classification, it takes into account the voting of the neighbors.…”

Section: Knn Algorithmmentioning

confidence: 99%

“…It takes into account the various factors that affect the performance of the network, such as the presence or absence of primary users and the total number of users. Through the use of machine learning algorithms, such as Logistic Regression, KNN Algorithm and SVM Algorithms, we can achieve better performance by providing an optimal boundary between presence and absence of primary users to achieve improved performance [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Experimental Evolution of Various Machine Learning Algorithms for Spectrum Sensing in Cognitive Radio Networks using Software Defined Radio

GINKAWAR¹,

Bhandari

2022

Preprint

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A cognitive radio is a wireless communication system that uses the spectral environment to improve the efficiency of its operations. One of the most important phases of this system is the spectrum sensing. This process involves detecting the presence of signals in a specific frequency band. This paper presents a variety of machine learning-based solutions that are designed to improve the efficiency of the spectrum sensing process. These include K-Nearest Neighbors (KNN), Logistic regression, and Support vector machine (SVM) algorithms. The paper also shows how these solutions can be used in cooperative spectrum sensing by using non-orthogonal multiple access (NOMA) technique. The main objective of these solutions is to provide a comprehensive view of the presence of primary users (PUs) in a real-time dataset which is prepared using software defined radio (SDR). They are then used to identify the state of each user in a given area by providing an optimal boundary between presence and absence of primary users to achieve improved performance. The performance of these algorithms is analyzed by taking into account various factors such as accuracy, sensitivity, specificity, F1_score and confusion matrix.

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“…Ref. [11], the author selects two ML techniques, SVM and K-Nearest Neighbor (KNN) [12][13][14], in which the detection probability is drawn using SVM and KNN algorithms, with a constant false positive probability. Based on the performance of the false alarm rate, the two ML methods are compared.…”

Section: Introductionmentioning

confidence: 99%

Supervised Learning Spectrum Sensing Method via Geometric Power Feature

Luo

Xiao

2023

Electronics

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In order to improve the spectrum sensing (SS) performance under a low Signal Noise Ratio (SNR), this paper proposes a supervised learning spectrum sensing method based on Geometric Power (GP) feature. The GP is used as the feature vector in the supervised learning spectrum sensing method for training and testing based on the actual captured data set. Experimental results show that the detection performance of the GP-based supervised learning spectrum sensing method is better than that of the Energy Statistics (ES) and Differential Entropy (DE)-based supervised learning spectrum sensing methods.

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An Optimized Spectrum Sensing Implementation Based on SVM, KNN and TREE Algorithms

Cited by 21 publications

References 20 publications

An ANN-based Intelligent Spectrum Sensing Algorithm for Space-based Satellite Networks

An ANN-based Intelligent Spectrum Sensing Algorithm for Space-based Satellite Networks

Experimental Evolution of Various Machine Learning Algorithms for Spectrum Sensing in Cognitive Radio Networks using Software Defined Radio

Supervised Learning Spectrum Sensing Method via Geometric Power Feature

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