Machine Learning Techniques Applied to Multiband Spectrum Sensing in Cognitive Radios

Molina-Tenorio, Yanqueleth; Prieto‐Guerrero, Alfonso; Aguilar-Gonzalez, Rafael; Boqué, Sílvia Ruiz

doi:10.3390/s19214715

Cited by 18 publications

(17 citation statements)

References 26 publications

(31 reference statements)

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“…This ensemble classifier is based on decision trees and using AdaBoost algorithm [ 96 ]. Wideband SS is tackled in [ 97 ], where three techniques are presented: neural networks, expectation maximization and k-means. The techniques are used to detect the presence of one or multiple PUs in a wideband spectrum.…”

Section: Learning Techniques For Spectrum Sensingmentioning

confidence: 99%

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

Nasser

Hassan

Chaaya

et al. 2021

Sensors

124

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Spectrum Sensing (SS) plays an essential role in Cognitive Radio (CR) networks to diagnose the availability of frequency resources. In this paper, we aim to provide an in-depth survey on the most recent advances in SS for CR. We start by explaining the Half-Duplex and Full-Duplex paradigms, while focusing on the operating modes in the Full-Duplex. A thorough discussion of Full-Duplex operation modes from collision and throughput points of view is presented. Then, we discuss the use of learning techniques in enhancing the SS performance considering both local and cooperative sensing scenarios. In addition, recent SS applications for CR-based Internet of Things and Wireless Sensors Networks are presented. Furthermore, we survey the latest achievements in Spectrum Sensing as a Service, where the Internet of Things or the Wireless Sensor Networks may play an essential role in providing the CR network with the SS data. We also discuss the utilisation of CR for the 5th Generation and Beyond and its possible role in frequency allocation. With the advancement of telecommunication technologies, additional features should be ensured by SS such as the ability to explore different available channels and free space for transmission. As such, we highlight important future research axes and challenging points in SS for CR based on the current and emerging techniques in wireless communications.

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Section: Learning Techniques For Spectrum Sensingmentioning

confidence: 99%

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

Nasser

Hassan

Chaaya

et al. 2021

Sensors

124

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“…ML methods are very effective when the data set is large, diverse, and fast changing. These algorithms give deep and predictive analysis of data, and they are classified into two big groups: supervised learning (classification and regression) and unsupervised learning (clustering techniques) [26,27]. Our objective is to classify legitimate SUs and MUs among all the SUs available in the environment.…”

Section: Proposed Support Vector Machine-based Mu Classification Algomentioning

confidence: 99%

“…The SVM can be used both for classification and regression problems. However, it is mostly used for classification [26]. It works on the basis of a hyperplane, which divides the different classes of data well.…”

Section: Proposed Support Vector Machine-based Mu Classification Algomentioning

confidence: 99%

Support Vector Machine-Based Classification of Malicious Users in Cognitive Radio Networks

Khan

Gul

et al. 2020

Wireless Communications and Mobile Computing

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Cognitive radio is an intelligent radio network that has advancement over traditional radio. The difference between the traditional radio and the cognitive radio is that all the unused frequency spectrum can be utilized to the best of available resources in the cognitive radio unlike the traditional radio. The core technology of cognitive radio is spectrum sensing, in which secondary users (SUs) opportunistically access the spectrum while avoiding interference to primary user (PU) channels. Various aspects of the spectrum sensing have been studied from the perspective of cognitive radio. Cooperative spectrum sensing (CSS) technique provides a promising performance, compared with individual sensing techniques. However, the existence of malicious users (MUs) highly degrades the performance of cognitive radio network (CRN) by sending falsified results to a fusion center (FC). In this paper, we propose a machine learning algorithm based on support vector machine (SVM) to classify legitimate SUs and MUs in the CRN. The proposed SVM-based algorithm is used for both classification and regression. It clearly classifies legitimate SUs and MUs by drawing a hyperplane on the base of maximal margin. After successful classification, the sensing results from the legitimate SUs are combined at the FC by utilizing Dempster-Shafer (DS) evidence theory. The effectiveness of the proposed SVM-based classification algorithm is demonstrated through simulations, compared with existing schemes.

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“…The authors of the current paper presented a previous study introducing a novel MBSS technique based on multiresolution analysis (MRA) [ 9 , 10 ], combined with machine learning (ML), for edge detection and with the Higuchi fractal dimension (DFH) [ 11 ] as a binary decision rule for distinguishing noise and a possible PU transmission. In this work [ 12 ], one of the three ML algorithms, used for the classification of the coefficients, was the K-means algorithm. By considering this algorithm, it was possible to obtain, on average, 98% certainty of detecting the beginning and end of a PU transmission, for an SNR greater than 0 dB.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology

Molina-Tenorio

Prieto‐Guerrero

Aguilar-Gonzalez

2021

Sensors

Self Cite

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In this work, a novel multiband spectrum sensing technique is implemented in the context of cognitive radios. This technique is based on multiresolution analysis (wavelets), machine learning, and the Higuchi fractal dimension. The theoretical contribution was developed before by the authors; however, it has never been tested in a real-time scenario. Hence, in this work, it is proposed to link several affordable software-defined radios to sense a wide band of the radioelectric spectrum using this technique. Furthermore, in this real-time implementation, the following are proposed: (i) a module for the elimination of impulsive noise, with which the appearance of sudden changes in the signal is reduced through the detail coefficients of the multiresolution analysis, and (ii) the management of different devices through an application that updates the information of each secondary user every 100 ms. The performance of these linked devices was evaluated with encouraging results: 95% probability of success for signal-to-noise ratio (SNR) values greater than 0 dB and just five samples (mean) in error of the edge detection (start and end) for a primary user transmission.

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Machine Learning Techniques Applied to Multiband Spectrum Sensing in Cognitive Radios

Cited by 18 publications

References 26 publications

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

Support Vector Machine-Based Classification of Malicious Users in Cognitive Radio Networks

Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology

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