scholarly journals Machine Learning Techniques Applied to Multiband Spectrum Sensing in Cognitive Radios

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4715 ◽  
Author(s):  
Yanqueleth Molina-Tenorio ◽  
Alfonso Prieto-Guerrero ◽  
Rafael Aguilar-Gonzalez ◽  
Silvia Ruiz-Boqué
Keyword(s):  
Machine Learning ◽  
Spectrum Sensing ◽  
Expectation Maximization ◽  
Cognitive Radios ◽  
Primary User ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Multiple Primary ◽  
Approximation Coefficients ◽  
Multiple Primary Users

In this work, three specific machine learning techniques (neural networks, expectation maximization and k-means) are applied to a multiband spectrum sensing technique for cognitive radios. All of them have been used as a classifier using the approximation coefficients from a Multiresolution Analysis in order to detect presence of one or multiple primary users in a wideband spectrum. Methods were tested on simulated and real signals showing a good performance. The results presented of these three methods are effective options for detecting primary user transmission on the multiband spectrum. These methodologies work for 99% of cases under simulated signals of SNR higher than 0 dB and are feasible in the case of real signals.

scholarly journals A Novel Multiband Spectrum Sensing Method Based on Wavelets and the Higuchi Fractal Dimension

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1322 ◽  
Author(s):  
Yanqueleth Molina-Tenorio ◽  
Alfonso Prieto-Guerrero ◽  
Rafael Aguilar-Gonzalez
Keyword(s):  
Fractal Dimension ◽  
Spectrum Sensing ◽  
Cognitive Radios ◽  
Primary User ◽  
Linear Measure ◽  
Signal To Noise ◽  
Primary Users ◽  
Multiple Primary ◽  
Higuchi Fractal Dimension ◽  
Multiple Primary Users

In this work, two novel methodologies for the multiband spectrum sensing in cognitive radios are implemented. Methods are based on the continuous wavelet transform (CWT) and the multiresolution analysis (MRA) to detect the edges of available holes in the considered wideband spectrum. Besides, MRA is also combined with the Higuchi fractal dimension (a non-linear measure) to establish the decision rule permitting the detection of the absence or presence of one or multiple primary users in the studied wideband spectrum. Methods were tested on simulated and real signals showing a good performance. The results present these two methods as effective options for detecting primary user activity on the multiband spectrum. The first methodology works for 95% of cases, while the second one presents 98% of effectivity under simulated signals of signal-to-noise ratios (SNR) higher than 0 dB.

scholarly journals Fast and accurate primary user detection with machine learning techniques for cognitive radio networks

2021 ◽  
Vol 21 (1) ◽  
pp. 472
Author(s):  
G. A. Pethunachiyar ◽  
B. Sankaragomathi
Keyword(s):  
Machine Learning ◽  
Spectrum Sensing ◽  
Orthogonal Frequency Division Multiplexing ◽  
Signal To Noise Ratio ◽  
Energy Detection ◽  
Primary User ◽  
Detection Algorithm ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques

<p class="IJASEITAbtract"><span>Spectrum decision is an important and crucial task for the secondary user to avail the unlicensed spectrum for transmission. Managing the spectrum is an efficient one for spectrum sensing. Determining the primary user presence in the spectrum is an essential work for using the licensed spectrum of primary user. The information which lacks in managing the spectrum are the information about the primary user presence, accuracy in determining the existence of user in the spectrum, the cost for computation and difficult in finding the user in low signal-to noise ratio (SNR) values. The proposed system overcomes the above limitations. In the proposed system, the various techniques of machine learning like decision tree, support vector machines, naive bayes, ensemble based trees, nearest neighbour’s and logistic regression are used for testing the algorithm. As a first step, the spectrum sensing is done in two stages with Orthogonal Frequency Division Multiplexing and Energy Detection algorithm at the various values of SNR. The results generated from the above algorithm is used for database generation. Next, the different machine learning techniques are trained and compared for the results produced by different algorithms with the characteristics like speed, time taken for training and accuracy in prediction. The accuracy and finding the presence of the user in the spectrum at low SNR values are achieved by all the algorithms. The computation cost of the algorithm differs from each other. Among the tested techniques, k-nearest neighbour (KNN) algorithm produces the better performance in a minimized time.</span></p>

Big Data-Based Spectrum Sensing for Cognitive Radio Networks Using Artificial Intelligence

2020 ◽  
pp. 146-159 ◽  
Author(s):  
Suriya Murugan ◽  
Sumithra M. G.
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Spectrum Utilization ◽  
Candidate Solution ◽  
Learning Techniques ◽  
Efficient Data

Cognitive radio has emerged as a promising candidate solution to improve spectrum utilization in next generation wireless networks. Spectrum sensing is one of the main challenges encountered by cognitive radio and the application of big data is a powerful way to solve various problems. However, for the increasingly tense spectrum resources, the prediction of cognitive radio based on big data is an inevitable trend. The signal data from various sources is analyzed using the big data cognitive radio framework and efficient data analytics can be performed using different types of machine learning techniques. This chapter analyses the process of spectrum sensing in cognitive radio, the challenges to process spectrum data and need for dynamic machine learning algorithms in decision making process.

scholarly journals Cognitive Spectrum Sensing with Multiple Primary Users in Rayleigh Fading Channels

2021 ◽  
Author(s):  
Salam Al-Juboori ◽  
Sattar J. Hussain ◽  
Xavier N. Fernando
Keyword(s):  
Fading Channels ◽  
Spectrum Sensing ◽  
Multipath Fading ◽  
Primary User ◽  
Multipath Fading Channels ◽  
Primary Users ◽  
Accurate Detection ◽  
Multiple Primary ◽  
Spherical Detector ◽  
Multiple Primary Users

Accurate detection of white spaces is crucial in cognitive radio networks. Initial investigations show that the accurate detection in a multiple primary users environment is challenging, especially under severe multipath conditions. Among many techniques, recently proposed eigenvalue-based detectors that use random matrix theories to eliminate the need of prior knowledge of the signals proved to be a solid approach. In this work, we study the effect of Rayleigh multipath fading channels on spectrum sensing in a multiple primary user environment for a pre-proposed detector called the spherical detector using the eigenvalue approach. Simulation results show interesting outcomes.

A RELATIONSHIP BETWEEN COGNITIVE INFORMATION PROCESSING IN LEARNING THEORY AND MACHINE LEARNING TECHNIQUES IN COGNITIVE RADIOS

2018 ◽  
Vol 5 ◽  
pp. 465-474 ◽  
Author(s):  
Zhaneta Tasheva ◽  
Rosen Bogdanov
Keyword(s):  
Machine Learning ◽  
Information Processing ◽  
Learning Theory ◽  
Cognitive Radios ◽  
Machine Learning Techniques ◽  
Learning Theories ◽  
Cognitive Approach ◽  
Cognitive Information Processing ◽  
Learning Techniques ◽  
Cognitive Information

The relationship between cognitivism as learning theory in education and machine learning is characterized in this survey paper. The cognitivism describes how learning occurs through internal processing of information and thus leads to understanding and retention. Cognitive information processing plays an active role to understand and process information that learner receives and relates it to already known and stored within learner’s memory. Thus, the cognitive approach defines learning as a change in knowledge which is stored in learner’s memory, and not a change in learner’s behaviour. In regard with importance of various learning problems to designing cognitive communications systems the two main classification categories of learning techniques are explained. Furthermore, the cognitive radio learning algorithms that have been proposed are described. Finally, the similarities and differences among the principles of learning theories and machine learning are discussed.

scholarly journals Case study of TV spectrum sensing model based on machine learning techniques

2021 ◽  
Author(s):  
Abdalaziz Mohammad ◽  
Faroq Awin ◽  
Esam Abdel-Raheem
Keyword(s):  
Machine Learning ◽  
Spectrum Sensing ◽  
Machine Learning Techniques ◽  
Model Based ◽  
Learning Techniques ◽  
Sensing Model
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