Spectrum Sensing for Smart Embedded Devices in Cognitive Networks using Machine Learning Algorithms

Saber, Mohamed; Rharras, Abdessamad El; Saadane, Rachid; Chehri, Abdellah; Hakem, Nadir; Kharraz, Hatim

doi:10.1016/j.procs.2020.09.311

Cited by 14 publications

(13 citation statements)

References 21 publications

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“…Since the operation of CR systems involves two processes, i.e., spectrum sensing and spectrum access, energy efficiency is paramount. Thus, for energy efficient operations, a low-cost low-power consumption implementation of spectrum sensing, based on realistic signals was proposed in [51]. The signals were generated using smart embedded devices with different modulation scheme types and the reception interface was an RTL-SDR dongle connected to a MATLAB software.…”

Section: Application Of Machine Learning In Spectrum Management 1) Cognitive Radio Networkmentioning

confidence: 99%

AI Meets CRNs: A Prospective Review on the Application of Deep Architectures in Spectrum Management

Hlophe

Maharaj

2021

IEEE Access

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The spectrum low utilization and high demand conundrum created a bottleneck towards fulfilling the requirements of next-generation networks. The cognitive radio (CR) technology was advocated as a de facto technology to alleviate the scarcity and under-utilization of spectrum resources by exploiting temporarily vacant spectrum holes of the licensed spectrum bands. As a result, the CR technology became the first step towards the intelligentization of mobile and wireless networks, and in order to strengthen its intelligent operation, the cognitive engine needs to be enhanced through the exploitation of artificial intelligence (AI) strategies. Since comprehensive literature reviews covering the integration and application of deep architectures in cognitive radio networks (CRNs) are still lacking, this article aims at filling the gap by presenting a detailed review that addresses the integration of deep architectures into the intricacies of spectrum management. This is a prospective review whose primary objective is to provide an indepth exploration of the recent trends in AI strategies employed in mobile and wireless communication networks. The existing reviews in this area have not considered the relevance of incorporating the mathematical fundamentals of each AI strategy and how to tailor them to specific mobile and wireless networking problems. Therefore, this review addresses that problem by detailing how deep architectures can be integrated into spectrum management problems. Beyond reviewing different ways in which deep architectures can be integrated into spectrum management, model selection strategies and how different deep architectures can be tailored into the CR space to achieve better performance in complex environments are then reported in the context of future research directions.

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Section: Application Of Machine Learning In Spectrum Management 1) Cognitive Radio Networkmentioning

confidence: 99%

AI Meets CRNs: A Prospective Review on the Application of Deep Architectures in Spectrum Management

Hlophe

Maharaj

2021

IEEE Access

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“…The results showed that the new model gave better accuracy compared to existing ones. In [18], the authors used SVM for spectrum sensing in order to detect signals presence in a particular frequency band. The results show that the SVM classifier achieves the highest detection performance compared to the other classifiers (ED and ANN).…”

Section: Related Workmentioning

confidence: 99%

“…Similarly, RF renders decision trees on data samples and then gets predictions from all of them, eventually picking the best answer by voting [29]. A fixed approach is more apparent than a single DT since it eliminates over-fitting by averaging outcomes [18]. Figure 5 shows the operational principles of the RF algorithm [24].…”

Section: Random Forest(rf)mentioning

confidence: 99%

Enhanced Epileptic Seizure diagnosis using EEG Signals with Support vector machine and Bagging Classifiers

Alrawashdeh

Al-Fawa’reh

Mardini

2021

Int. j. commun. netw. inf. secur.

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Many approaches have been proposed using Electroencephalogram (EEG) to detect epilepsy seizures in their early stages. Epilepsy seizure is a severe neurological disease. Practitioners continue to rely on manual testing of EEG signals. Artificial intelligence (AI) and Machine Learning (ML) can effectively deal with this problem. ML can be used to classify EEG signals employing feature extraction techniques. This work focuses on automated detection for epilepsy seizures using ML techniques. Various algorithms are investigated, such as Bagging, Decision Tree (DT), Adaboost, Support vector machine (SVM), K-nearest neighbors(KNN), Artificial neural network(ANN), Naïve Bayes, and Random Forest (RF) to distinguish injected signals from normal ones with high accuracy. In this work, 54 Discrete wavelet transforms (DWTs) are used for feature extraction, and the similarity distance is applied to identify the most powerful features. The features are then selected to form the features matrix. The matrix is subsequently used to train ML. The proposed approach is evaluated through different metrics such as F-measure, precision, accuracy, and Recall. The experimental results show that the SVM and Bagging classifiers in some data set combinations, outperforming all other classifiers

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“…In this situation, the performance of multiple hypothesis testing based energy detection will decrease seriously. Therefore, we think of using SVM to exploit a nonlinear decision to replace the linear threshold obtained by hypothesis testing; since among all the above works [11][12][13][14][15], SVM based algorithm always has the best performance compared with the other mentioned ML algorithms. The SVM classifier has been shown to possess excellent classification capability.…”

Section: A Motivationmentioning

confidence: 99%

“…And comparative simulation results clearly reveal that the proposed methods outperform the existing state-of-the-art CSS techniques. In [13], Saber et al propose a low power consumption and low cost implementation of spectrum sensing operation by using four machine learning (ML) algorithms: the artificial neural networks (ANN), SVM, Decision Trees (TREE), and KNN. In [14], to addresses the problem of spectrum sensing under multiple primary users condition, the authors uses multi-class SVM.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Compressive Wideband Spectrum Sensing Algorithm Based on Least Squares Support Vector Machine

Ren

Chen

et al. 2021

IEEE Access

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Most of the compressive wideband spectrum sensing algorithms need to recover the spectrum, which require high computational complexity. Recently, a novel algorithm for compressive wideband sensing without spectrum recovery (NoR) was proposed. Its computational complexity is several orders of magnitude less than that of algorithms that need spectrum recovery. However, enabling by structureconstrained assumption of sparse spectrum, NoR may fail. In order to expand its scope of application while reducing the computational complexity as much as possible, we propose an adaptive sensing (ADP) algorithm that is a powerful hybrid of the no recovery and partial recovery (PR) algorithms. The ADP algorithm adaptively chooses the no recovery or partial recovery scheme depending on the situation learned by the least squares support vector machine (LS-SVM). By simulation and analysis, compared with NoR, PR and another excellent algorithm (orthogonal matching pursuit), the ADP suits better for practical applications.

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Spectrum Sensing for Smart Embedded Devices in Cognitive Networks using Machine Learning Algorithms

Cited by 14 publications

References 21 publications

AI Meets CRNs: A Prospective Review on the Application of Deep Architectures in Spectrum Management

AI Meets CRNs: A Prospective Review on the Application of Deep Architectures in Spectrum Management

Enhanced Epileptic Seizure diagnosis using EEG Signals with Support vector machine and Bagging Classifiers

An Adaptive Compressive Wideband Spectrum Sensing Algorithm Based on Least Squares Support Vector Machine

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