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

Spectrum sensing is an essential component in cognitive radios (CR). Machine learning (ML) algorithms are powerful techniques for designing a promising spectrum sensing model. In this work, the supervised ML algorithms, support vector machine (SVM), k-nearest neighbor (kNN), and decision tree (DT) are applied to detect the existence of primary users (PU) over the TV band. Moreover, the Principal Component Analysis (PCA) is incorporated to speed up the learning of the classifiers. Furthermore, the ensemble classification-based approach is employed to enhance the classifier predictivity and performance. Simulation results have shown that the highest performance is achieved by the ensemble classifier. Moreover, simulation results have shown that employing PCA reduces the duration of training while maintaining the performance

Applications of Machine Learning in Spectrum Sensing for Cognitive Radios

Spectrum sensing is an essential component in cognitive radios. The machine learning (ML) approach is part of artificial intelligence which develops systems capable of learning and improving from experience. ML algorithms are promising techniques for spectrum sensing as a favored solution to tackle the limitations of conventional spectrum sensing techniques while improving detection performance. The supervised ML algorithms, support vector machine (SVM), k-nearest neighbor (kNN), decision tree (DT), and ensemble are applied to detect the existence of primary users (PUs) in the TV spectrum band. This is accomplished by building classifiers using the collected data for the TV spectrum over different locations in the city of Windsor, Ontario. Then, the dimensionality reduction technique named Principal Component Analysis (PCA) is incorporated to reduce the duration of training and testing of the model, as well as reduce the risk of overfitting. This is achieved by transforming the input data into a lower-dimensional representation, which is known as the principal components. The Ensemble classification-based approach is employed to enhance the classifier predictivity and performance. Furthermore, the performance of the Ensemble classification method is compared with SVM, kNN, and DT classifiers. Simulation results have shown that the highest performance is achieved by combining multiple classifiers, i.e., the Ensemble, therefore, the detection performance has significantly improved. Simulation results have shown the impact of employing PCA on lowering the duration of training while maintaining the performance

Byzantine Attack and Defense in Cognitive Radio Networks: A Survey

The Byzantine attack in cooperative spectrum sensing (CSS), also known as the spectrum sensing data falsification (SSDF) attack in the literature, is one of the key adversaries to the success of cognitive radio networks (CRNs). In the past couple of years, the research on the Byzantine attack and defense strategies has gained worldwide increasing attention. In this paper, we provide a comprehensive survey and tutorial on the recent advances in the Byzantine attack and defense for CSS in CRNs. Specifically, we first briefly present the preliminaries of CSS for general readers, including signal detection techniques, hypothesis testing, and data fusion. Second, we analyze the spear and shield relation between Byzantine attack and defense from three aspects: the vulnerability of CSS to attack, the obstacles in CSS to defense, and the games between attack and defense. Then, we propose a taxonomy of the existing Byzantine attack behaviors and elaborate on the corresponding attack parameters, which determine where, who, how, and when to launch attacks. Next, from the perspectives of homogeneous or heterogeneous scenarios, we classify the existing defense algorithms, and provide an in-depth tutorial on the state-of-the-art Byzantine defense schemes, commonly known as robust or secure CSS in the literature. Furthermore, we highlight the unsolved research challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral

Analysis of Spectrum Occupancy Using Machine Learning Algorithms

In this paper, we analyze the spectrum occupancy using different machine learning techniques. Both supervised techniques (naive Bayesian classifier (NBC), decision trees (DT), support vector machine (SVM), linear regression (LR)) and unsupervised algorithm (hidden markov model (HMM)) are studied to find the best technique with the highest classification accuracy (CA). A detailed comparison of the supervised and unsupervised algorithms in terms of the computational time and classification accuracy is performed. The classified occupancy status is further utilized to evaluate the probability of secondary user outage for the future time slots, which can be used by system designers to define spectrum allocation and spectrum sharing policies. Numerical results show that SVM is the best algorithm among all the supervised and unsupervised classifiers. Based on this, we proposed a new SVM algorithm by combining it with fire fly algorithm (FFA), which is shown to outperform all other algorithms.Comment: 21 pages, 6 figure

Machine learning techniques applied to multiband spectrum sensing in cognitive radios

This research received funding of the Mexican National Council of Science and Technology (CONACYT), Grant (no. 490180). Also, this work was supported by the Program for Professional Development Teacher (PRODEP).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 signalsPeer ReviewedPostprint (published version