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http://cognitive-radio.ece.unm.edu/papers/conf_2009_06_05.pdf
Robust Support Vector Machine Spectrum Estimation in Cognitive Radio Thomas D. Atwood*(1), Manel Martínez-Ramon(2), and Christos G. Christodoulou(1) (1) Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico
https://www.researchgate.net/publication/224122496_Signal_Classification_with_an_SVM-FFT_Approach_for_Feature_Extraction_in_Cognitive_Radio
Signal Classification with an SVM-FFT Approach for Feature Extraction in Cognitive Radio. ... Support Vector Machine Spectrum Estimation in Cognitive Radio," in. ... "A robust support vector.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6339174/
Jan 02, 2019 · For instance, the authors of [42,43] proposed a spectrum sensing model for cognitive radio based on both K-means and support vector machine. First, K-means is applied to discover primary users’ transmission patterns and statistics.Cited by: 16
https://ieeexplore.ieee.org/document/7391780/
Abstract: This paper addresses the problem of spectrum sensing in cognitive radio networks under multiple primary users condition using multi-class support vector machine (SVM) algorithms. First, we formulated the spectrum sensing problem under multiple primary users scenario as a multiple class signal detection problem where each class is ...
http://cognitive-radio.ece.unm.edu/papers/conf_2009_10_01.pdf
for spectrum estimation are described, and in section III the feature extraction used for signal classification in conbination with the spectrum SVM estimation is presented. Section IV presents the results of this combination in some modulation classification experiments. II. SPECTRUM ESTIMATION BASED ON SUPPORT VECTOR MACHINES
https://thescipub.com/PDF/jcssp.2013.235.243.pdf
Keywords: Cognitive Radio, Second-Order Statistics, Support Vector Machine, Digital Modulation 1. INTRODUCTION A number of definitions can be found to describe Software Defined Radio, also known as Software Radio or SDR. Software Defined Radio is defined as: “Radio in which some or all of the physical layer functions are
https://www.researchgate.net/publication/331264092_Performance_Analysis_of_Support_Vector_Machine-Based_Classifier_for_Spectrum_Sensing_in_Cognitive_Radio_Networks
PDF On Oct 1, 2018, Sana Ullah Jan and others published Performance Analysis of Support Vector Machine-Based Classifier for Spectrum Sensing in Cognitive Radio Networks
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3988858/
Spectrum mobility as an essential issue has not been fully investigated in mobile cognitive radio networks (CRNs). In this paper, a novel support vector machine based spectrum mobility prediction (SVM-SMP) scheme is presented considering time-varying and space-varying characteristics simultaneously in mobile CRNs.Cited by: 5
https://www.researchgate.net/publication/259178458_Eigenvalue_and_Support_Vector_Machine_Techniques_for_Spectrum_Sensing_in_Cognitive_Radio_Networks
Machine learning techniques in general and support vector machine (SVM) are essential in cognitive sensing for efficient radio spectrum utilization such that CRs can learn the environment ...
https://www.radioeng.cz/fulltexts/2015/15_01_0192_0198.pdf
Cognitive radio, auto-correlation function, machine learning, Support Vector Machine, spectrum sens-ing, statistical signal processing, opportunistic dynamic spectrum access 1. Introduction The concept of cognitive radio raised a question on the effectiveness of fixed spectrum access and indicate the need to change the spectrum assignment policy.
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